fitcauto

自动选择具有优化超参数的分类模型

全部折叠在页面中

语法

MDL = fitcauto（TBL，ResponseVarName）

Mdl = fitcauto(资源描述、公式)

MDL = fitcauto（TBL，Y）

Mdl = fitcauto (X, Y)

Mdl = fitcauto (___、名称、值)

[Mdl, OptimizationResults] = fitcauto (___)

描述

给定预测和响应数据，fitcauto自动尝试选择具有不同超参数值分类模型类型。该功能使用贝叶斯优化选择模型及其超参数值，并计算交叉验证分类错误的每个模型。经过优化完成后，fitcauto返回模式，培养对整个数据集，也有望最好的分类新的数据。您可以使用预测和损失返回模型的对象的功能来新数据进行分类，并计算测试集分类误差，分别。

采用fitcauto当你不确定哪个分类类型最适合您的数据。有关分类模型的调整超参数替代方法的信息，请参阅另类功能。

例子

MDL= fitcauto（资源描述,ResponseVarName)返回分类模型MDL与调整超参数。桌子资源描述包含预测变量和响应变量，其中ResponseVarName是响应变量的名称。

MDL= fitcauto（资源描述,公式)使用公式指定响应变量和预测变量的变量之间考虑资源描述。

MDL= fitcauto（资源描述,Y)使用表预测变量资源描述和类别标签在矢量Y。

例子

MDL= fitcauto（X,Y)使用矩阵预测变量X和类别标签在矢量Y。

MDL= fitcauto（___,名称，值)使用一个或除了在任何先前的语法输入参数组合多个名称 - 值对的参数指定的选项。例如，使用HyperparameterOptimizationOptions名称-值对参数，用于指定如何执行贝叶斯优化。

例子

(MDL,OptimizationResults] = fitcauto（___)此外回报OptimizationResults，一个BayesianOptimization对象包含模型选择和超参数调整过程的结果。

例子

全部收缩

使用表数据自动选择分类器

开立真实脚本

采用fitcauto自动地选择具有优化的超参数，特定的预测和响应数据存储在一个表中的分类模型。

加载数据

加载carbig数据集，它包含了20世纪70年代和80年代初生产的汽车的测量数据。

加载carbig

根据汽车是否产自美国来分类。

原点=分类（cellstr（原点））;原点= mergecats（产地，{“法国”,'日本','德国',...“瑞典”,'意大利','英国'},“NotUSA”);

创建一个包含预测变量的表促进,位移等等，以及响应变量起源。

汽车=表(加速度、位移、马力、...Model_Year，MPG，重量，产地）;

分区数据

将数据划分为训练集和测试集。使用大约80%的观察值进行模型选择和超参数调优过程，并使用20%的观察值测试所返回的最终模型的性能fitcauto。采用cvpartition对数据进行分区。

RNG（“默认”)％对于数据分区的再现性c = cvpartition(起源、“坚持”,0.2);trainingIdx =培训(c);％训练集指标carsTrain =汽车(trainingIdx:);testIdx =测试(c);％测试集指标carsTest =汽车（testIdx，:);

跑fitcauto

通过训练数据fitcauto。默认，fitcauto确定要尝试的适当模型类型，使用贝叶斯优化找到好的超参数值，并返回一个经过训练的模型MDL具有最佳的预期性能。

预计这一过程需要一段时间。为了加快优化过程中，考虑指定并行运行的优化，如果你有一个并行计算工具箱™许可证。要做到这一点，通“HyperparameterOptimizationOptions”、结构(UseParallel,真的)来fitcauto作为一个名称 - 值对的参数。

MDL = fitcauto（carsTrain，'起源');

警告:在优化Naive Bayes 'Width'参数时，建议首先对所有数值谓词进行标准化。如果您这样做了，请忽略此警告。

| ==================================================================================================================== ||ITER |EVAL |目的|目的|BestSoFar |BestSoFar |学员|超参数：值| | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 1 | Best | 0.12923 | 12.533 | 0.12923 | 0.12923 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 7 |

| 2 |接受| 0.18269 | 0.6441 | 0.12923 | 0.12923 | knn | NumNeighbors: 3 |

|3 |接受|0.23397 |0.12905 |0.12923 |0.20782 |KNN |NumNeighbors：91 |

|4 |接受|0.16308 |12.228 |0.12923 |0.14852 |合奏|方法：LogitBoost || | | | | | | | NumLearningCycles: 274 | | | | | | | | | MinLeafSize: 15 |

| 5 |接受| 0.20833 | 0.13612 | 0.12923 | 0.14852 | knn | NumNeighbors: 4 |

| 6 |接受| 0.22115 | 0.13461 | 0.12923 | 0.14852 | knn | NumNeighbors: 28 |

|7 |接受|0.16923 |0.25707 |0.12923 |0.14852 |树|MinLeafSize：105 |

|8 |接受|0.37179 |0.64601 |0.12923 |0.14852 |SVM |BoxConstraint：0.022186 || | | | | | | | KernelScale: 0.085527 |

| 9 |接受| 0.37179 | 0.12828 | 0.12923 | 0.14852 |支持向量机| BoxConstraint: 0.045899 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 0.0024758 |7

| 10 |接受| 0.24615 | 0.99945 | 0.12923 | 0.14852 | nb |分布名:kernel | | |0 |1 |2 |3 |4 |5 |6 Width: 1.1327 |7

|11 |接受|0.16923 |0.098106 |0.12923 |0.14852 |树|MinLeafSize：78 |

|12 |接受|0.26923 |0.11213 |0.12923 |0.14852 |SVM |BoxConstraint：11.063 || | | | | | | | KernelScale: 15.114 |

| 13 |最佳| 0.12615 | 0.10159 | 0.12615 | 0.14852 |树| MinLeafSize: 3 |

| 14 |接受| 0.21154 | 0.096928 | 0.12615 | 0.14852 | knn | NumNeighbors: 2 |

| 15 |接受| 0.13538 | 0.10336 | 0.12615 | 0.15014 |树| MinLeafSize: 1 |

| 16 |接受| 0.13538 | 0.096076 | 0.12615 | 0.1482 |树| MinLeafSize: 2 |

|17 |最佳|0.12308 |10.115 |0.12308 |0.13678 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 208 | | | | | | | | | MinLeafSize: 10 |

| 18 |接受| 0.37179 | 0.13789 | 0.12308 | 0.13678 |支持向量机| BoxConstraint: 116.46 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 0.52908 |7

|19 |接受|0.22769 |0.16645 |0.12308 |0.13678 |NB |DistributionNames：正常|| | | | | | | | Width: NaN |

|20 |接受|0.22115 |0.087468 |0.12308 |0.13678 |KNN |NumNeighbors：8 |

| ==================================================================================================================== ||ITER |EVAL |目的|目的|BestSoFar |BestSoFar |学员|超参数：值| | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 21 | Accept | 0.37179 | 0.13455 | 0.12308 | 0.13678 | svm | BoxConstraint: 45.341 | | | | | | | | | KernelScale: 0.76949 |

|22 |接受|0.12615 |0.10641 |0.12308 |0.13678 |树|MinLeafSize：3 |

| 23 |最佳| 0.10769 | 0.075704 | 0.10769 | 0.13678 |树| minleaf8: 5 |

| 24 |接受| 0.22769 | 0.28513 | 0.10769 | 0.13678 | nb |分布名:kernel | | |0 |1 |2 |3 |4 |5 |6 Width: 0.42571 |7

|25 |接受|0.12615 |0.072307 |0.10769 |0.13846 |树|MinLeafSize：11 |

| 26 |接受| 0.13782 | 0.084552 | 0.10769 | 0.13846 |支持向量机| BoxConstraint: 9.7286 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 293.41 |7

| 27 |接受| 0.22769 | 0.098663 | 0.10769 | 0.13846 | nb |分布名:normal | bb9 |0 |1 |2 |3 |4 |5 |6 Width: NaN |7

|28 |接受|0.21795 |0.087931 |0.10769 |0.13846 |KNN |NumNeighbors：42 |

|29 |接受|0.24308 |0.26915 |0.10769 |0.13846 |NB |DistributionNames：内核|| | | | | | | | Width: 4.4662 |

|30 |接受|0.16308 |11.221 |0.10769 |0.13846 |合奏|方法：LogitBoost || | | | | | | | NumLearningCycles: 267 | | | | | | | | | MinLeafSize: 131 |

| 31 |接受| 0.24308 | 0.26485 | 0.10769 | 0.13846 | nb |分布名:kernel | | |0 |1 |2 |3 |4 |5 |6 Width: 0.66296 |7

| 32 |接受| 0.22115 | 0.067784 | 0.10769 | 0.13846 | knn | NumNeighbors: 28 |

|33 |接受|0.13846 |0.11902 |0.10769 |0.13714 |树|MinLeafSize：25 |

| 34 |接受| 0.21474 | 0.07273 | 0.10769 | 0.13714 | knn | NumNeighbors: 14 |

| 35 |接受| 0.16615 | 9.2855 | 0.10769 | 0.13714 |集合|方法:LogitBoost | | |0 |1 |2 |3 |4 |5 |6 NumLearningCycles: 215 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 13 |6

|36 |接受|0.15077 |11.786 |0.10769 |0.13714 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 31 |

|37 |接受|0.22769 |0.081882 |0.10769 |0.13714 |NB |DistributionNames：正常|| | | | | | | | Width: NaN |

| 38 |接受| 0.37179 | 0.07709 | 0.10769 | 0.13714 |支持向量机| BoxConstraint: 0.0073633 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 774.33 |7

|39 |接受|0.16923 |0.077139 |0.10769 |0.1325 |树|MinLeafSize：82 |

| 40 |接受| 0.20833 | 0.087004 | 0.10769 | 0.1325 | knn | NumNeighbors: 4 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | Iter | Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |结果| | |运行时(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | 41接受| 0.16308 | 12.164 | 0.10769 | 0.1325 |合奏|方法:LogitBoost | | | | | | | | | NumLearningCycles: 274 | |0 |1 |2 |3 |4 |5 |6 |7 MinLeafSize: 150 |8

| 42 |接受| 0.22462 | 0.28662 | 0.10769 | 0.1325 | nb |分布名:kernel | | |0 |1 |2 |3 |4 |5 |6 Width: 121.64 |7

|43 |接受|0.17846 |10.427 |0.10769 |0.1325 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 229 | | | | | | | | | MinLeafSize: 117 |

|44 |接受|0.16923 |0.088318 |0.10769 |0.12874 |树|MinLeafSize：84 |

| 45 |接受| 0.22769 | 0.079169 | 0.10769 | 0.12874 | nb |分布名称:正常| bb9 |0 |1 |2 |3 |4 |5 |6宽度:NaN |7

| 46 |接受| 0.22769 | 0.065898 | 0.10769 | 0.12874 | nb |分布名称:正常| bb9 |0 |1 |2 |3 |4 |5 |6宽度:NaN |7

|47 |接受|0.16615 |9.3444 |0.10769 |0.12874 |合奏|方法：LogitBoost || | | | | | | | NumLearningCycles: 212 | | | | | | | | | MinLeafSize: 49 |

| 48 |接受| 0.14154 | 13.721 | 0.10769 | 0.12874 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:288 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 25 |6

|49 |接受|0.23077 |0.2725 |0.10769 |0.12874 |NB |DistributionNames：内核|| | | | | | | | Width: 73.249 |

| 50 |接受| 0.37179 | 0.11629 | 0.10769 | 0.12874 |支持向量机| BoxConstraint: 0.0036501 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 1.0504 |7

| 51 |接受| 0.21474 | 0.14443 | 0.10769 | 0.12874 |支持向量机| BoxConstraint: 64.859 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 23.779 |7

| 52 |接受| 0.37179 | 0.11504 | 0.10769 | 0.12874 |支持向量机| BoxConstraint: 0.16622 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 4.4901 |7

|53 |接受|0.25846 |0.26577 |0.10769 |0.12874 |NB |DistributionNames：内核|| | | | | | | | Width: 0.079498 |

|54 |接受|0.21154 |0.07558 |0.10769 |0.12874 |KNN |NumNeighbors：2 |

|55 |接受|0.12308 |11.525 |0.10769 |0.12874 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 234 | | | | | | | | | MinLeafSize: 8 |

|56 |接受|0.36538 |0.11498 |0.10769 |0.12874 |SVM |BoxConstraint：271.6 || | | | | | | | KernelScale: 2.743 |

| 57 |接受| 0.16615 | 9.9644 | 0.10769 | 0.12874 |集合|方法:LogitBoost | | |0 |1 |2 |3 |5 |6数字循环:248 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 117 |6

|58 |接受|0.37179 |0.10707 |0.10769 |0.12874 |SVM |BoxConstraint：7.5785 || | | | | | | | KernelScale: 0.0066815 |

|59 |接受|0.37179 |0.10387 |0.10769 |0.12874 |SVM |BoxConstraint：0.0017765 || | | | | | | | KernelScale: 0.86786 |

| 60 |接受| 0.37179 | 0.11451 | 0.10769 | 0.12874 |支持向量机| BoxConstraint: 0.011465 | | |0 |1 |2 |3 |4 |5 |6 KernelScale: 0.02747 |7

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | Iter | Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |结果| | |运行时(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 61 | |接受0.12308 | 0.075995 | 0.10769 | 0.12862 | |树MinLeafSize: 12 |

|62 |接受|0.29167 |0.12678 |0.10769 |0.12862 |SVM |BoxConstraint：11.939 || | | | | | | | KernelScale: 11.002 |

| 63 |接受| 0.21795 | 0.085439 | 0.10769 | 0.12862 | knn | NumNeighbors: 6 |

| 64 |接受| 0.18269 | 0.075299 | 0.10769 | 0.12862 | knn | NumNeighbors: 3 |

|65 |接受|0.12615 |0.09094 |0.10769 |0.1273 |树|MinLeafSize：3 |

| 66 |接受| 0.16923 | 0.074034 | 0.10769 | 0.12662 |树| MinLeafSize: 56 |

| 67 |接受| 0.1891 | 0.068062 | 0.10769 | 0.12662 | knn | NumNeighbors: 1 |

| 68 |接受| 0.11692 | 12.685 | 0.10769 | 0.12662 |集合|方法:袋装| bb9 |0 |1 |2 |3 |5 |6数字循环:270 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 4 |6

| 69 |接受| 0.22769 | 0.08331 | 0.10769 | 0.12662 | nb |分布名称:正常| bb9 |0 |1 |2 |3 |4 |5 |6宽度:NaN |7

| 70 |接受| 0.37231 | 0.23243 | 0.10769 | 0.12662 | nb |分布名:kernel | | |0 |1 |2 |3 |4 |5 |6 Width: 1629.5 |7

|71 |接受|0.16923 |0.07617 |0.10769 |0.12684 |树|MinLeafSize：61 |

| 72 |接受| 0.22769 | 0.076927 | 0.10769 | 0.12684 | nb |分布名:normal | bb9 |0 |1 |2 |3 |4 |5 |6 Width: NaN |7

| 73 |接受| 0.16308 | 9.7631 | 0.10769 | 0.12684 |集合|方法:LogitBoost | | |0 |1 |2 |3 |4 |5 |6 NumLearningCycles: 217 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 70 |6

| 74 |接受| 0.10769 | 12.552 | 0.10769 | 0.12684 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| 75 |接受| 0.21474 | 0.091663 | 0.10769 | 0.12684 | knn | NumNeighbors: 49 |

| 76 |接受| 0.11077 | 10.027 | 0.10769 | 0.12684 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:221 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 5 |6

|77 |接受|0.16 |8.1869 |0.10769 |0.12684 |合奏|方法：LogitBoost || | | | | | | | NumLearningCycles: 203 | | | | | | | | | MinLeafSize: 1 |

|78 |接受|0.11385 |10.992 |0.10769 |0.12684 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 228 | | | | | | | | | MinLeafSize: 2 |

| 79 |接受| 0.15692 | 12.232 | 0.10769 | 0.12684 |集合|方法:LogitBoost | | |0 |1 |2 |3 |5 |6数字学习循环:293 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 80 |接受| 0.12923 | 10.523 | 0.10769 | 0.12684 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:222 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 5 |6

| ==================================================================================================================== ||ITER |EVAL |目的|目的|BestSoFar |BestSoFar |学员|超参数：值| | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 81 | Accept | 0.12308 | 11.281 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | MinLeafSize: 11 |

| 82 |接受| 0.11385 | 14.304 | 0.10769 | 0.12684 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:290 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 3 |6

|83 |接受|0.10769 |12.236 |0.10769 |0.11724 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 1 |

| 84 |最佳| 0.10154 | 12.57 | 0.10154 | 0.11441 |集合|方法:袋装| | |0 |1 |1 |3 |5 |6数字循环:256 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|85 |接受|0.16308 |11.827 |0.10154 |0.11544 |合奏|方法：LogitBoost || | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |

|86 |接受|0.10769 |14.658 |0.10154 |0.11317 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 295 | | | | | | | | | MinLeafSize: 2 |

| 87 |接受| 0.10769 | 12.049 | 0.10154 | 0.11209 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:255 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| 88 |接受| 0.10462 | 12.346 | 0.10154 | 0.11125 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:261 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|89 |接受|0.11077 |12.551 |0.10154 |0.10964 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 263 | | | | | | | | | MinLeafSize: 1 |

| 90 |接受| 0.10769 | 12.461 | 0.10154 | 0.10895 |集合|方法:袋装| bb9 |0 |1 |2 |3 |5 |6数字循环:257 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|91 |接受|0.10769 |12.168 |0.10154 |0.10907 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 1 |

|92 |接受|0.10462 |12.397 |0.10154 |0.10811 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |

| 93 |接受| 0.16308 | 11.293 | 0.10154 | 0.10882 |集合|方法:LogitBoost | | |0 |1 |2 |3 |4 |5 |6 NumLearningCycles: 255 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 94 |接受| 0.10769 | 12.364 | 0.10154 | 0.10734 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:255 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 95 |接受| 0.11385 | 12.225 | 0.10154 | 0.10821 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:255 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|96 |接受|0.10769 |12.369 |0.10154 |0.10809 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |

| 97 |接受| 0.11077 | 12.488 | 0.10154 | 0.10714 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:258 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|98 |接受|0.10769 |12.439 |0.10154 |0.10802 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |

|99 |接受|0.10769 |12.361 |0.10154 |0.1069 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |

| 100 |接受| 0.11692 | 12.373 | 0.10154 | 0.10736 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:256 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | Iter | Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |结果| | |运行时(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 101 | |接受0.16308 | 10.816 | 0.10154 | 0.10799 |合奏|方法:LogitBoost | | | | | | | | | bb8 NumLearningCycles: 257 | |0 |1 |2 |3 |4 |5 |6 |7 MinLeafSize: 1 |8

| 102 |接受| 0.11385 | 9.9363 | 0.10154 | 0.10789 |集合|方法:包| bb9 |0 |1 |2 |3 |4 |5 bb16 NumLearningCycles: 210 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 103 |接受| 0.11385 | 10.353 | 0.10154 | 0.11056 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:256 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 104 |接受| 0.12 | 10.401 | 0.10154 | 0.11008 |集合|方法:包| bb9 |0 |1 |2 |3 |4 |5 bb16数字循环:257 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|105 |接受|0.11077 |11.72 |0.10154 |0.10913 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 293 | | | | | | | | | MinLeafSize: 1 |

|106 |接受|0.10769 |10.452 |0.10154 |0.10878 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |

| 107 |接受| 0.10769 | 10.517 | 0.10154 | 0.10934 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|108 |接受|0.11077 |8.3766 |0.10154 |0.10939 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 207 | | | | | | | | | MinLeafSize: 1 |

| 109 |接受| 0.10769 | 10.549 | 0.10154 | 0.10883 |集合|方法:包| bb9 |0 |1 |1 |3 |5 |6 NumLearningCycles: 261 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|110 |接受|0.11692 |10.478 |0.10154 |0.10961 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 259 | | | | | | | | | MinLeafSize: 4 |

|111 |接受|0.11385 |12.05 |0.10154 |0.10869 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 295 | | | | | | | | | MinLeafSize: 2 |

| 112 |接受| 0.12 | 11.95 | 0.10154 | 0.1083 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:294 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 113 |接受| 0.11385 | 10.329 | 0.10154 | 0.10891 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:255 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|114 |接受|0.11077 |11.196 |0.10154 |0.10922 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 2 |

| 115 |接受| 0.10462 | 12.305 | 0.10154 | 0.10804 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|116 |接受|0.11385 |12.352 |0.10154 |0.10886 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 4 |

| 117 |最佳| 0.098462 | 0.098462 | 0.10878 |集合|方法:包| | |0 |1 |2 |3 |4 |5 bb16多循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 118 |接受| 0.10769 | 9.6282 | 0.098462 | 0.10917 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 119 |接受| 0.11077 | 0.098462 | 0.10922 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 120 |接受| 0.12615 | 9.0029 | 0.098462 | 0.10942 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | Iter | Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |结果| | |运行时(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 121 | |接受0.11385 | 10.752 | 0.098462 | 0.10963 |合奏|方法:装袋| | | | | | | | | | NumLearningCycles: 257 | |0 |1 |2 |3 |4 |5 |6 |7 MinLeafSize: 2 |8

|122 |接受|0.10769 |10.986 |0.098462 |0.1089 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |

| 123 |接受| 0.10462 | 10.936 | 0.098462 | 0.10864 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 124 |接受| 0.11077 | 10.384 | 0.098462 | 0.10889 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 125 |接受| 0.10769 | 10.391 | 0.098462 | 0.10903 |集合|方法:包| bb9 |0 |1 |2 |3 |4 |5 bb16 NumLearningCycles: 257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|126 |接受|0.11385 |8.3687 |0.098462 |0.10863 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 202 | | | | | | | | | MinLeafSize: 1 |

|127 |接受|0.11385 |10.703 |0.098462 |0.10838 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 261 | | | | | | | | | MinLeafSize: 1 |

|128 |接受|0.11077 |9.8426 |0.098462 |0.10872 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 243 | | | | | | | | | MinLeafSize: 1 |

|129 |接受|0.11077 |8.1229 |0.098462 |0.10861 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 202 | | | | | | | | | MinLeafSize: 1 |

| 130 |接受| 0.11385 | 10.625 | 0.098462 | 0.10918 |集合|方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:256 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 131 |接受| 0.11077 | 10.54 | 0.098462 | 0.10936 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:254 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 132 |接受| 0.11077 | 10.936 | 0.098462 | 0.10902 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 133 |接受| 0.12923 | 10.934 | 0.098462 | 0.1098 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|134 |接受|0.11077 |10.599 |0.098462 |0.10985 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |

| 135 |接受| 0.10769 | 8.3652 | 0.098462 | 0.10981 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:200 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|136 |接受|0.10769 |8.6695 |0.098462 |0.1097 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 2 |

| 137 |接受| 0.11385 | 8.1568 | 0.098462 | 0.10997 |集合|的方法:包| | |0 |1 |2 |3 |5 |6数字循环:200 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 138 |接受| 0.10769 | 0.098462 | 0.10951 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字循环:200 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| 139 |接受| 0.11385 | 8.1919 | 0.098462 | 0.11039 |集合|方法:袋装| | |0 |1 |2 |3 |5 |6数字循环:200 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 140 |接受| 0.11385 | 11.537 | 0.098462 | 0.10919 |集合|的方法:包| bb9 |0 |1 |2 |3 |5 |6数字循环:261 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | Iter | Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |结果| | |运行时(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 141 | |接受0.10769 | 8.5051 | 0.098462 | 0.10985 |合奏|方法:袋装| | | | | | | | | NumLearningCycles: 201 | |0 |1 |2 |3 |4 |5 |6 |7 MinLeafSize: 2 |8

| 142 |接受| 0.11692 | 9.7003 | 0.098462 | 0.10953 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字循环:236 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| 143 |接受| 0.10462 | 11.018 | 0.098462 | 0.10932 |集合|的方法:包| | |0 |1 |2 |3 |5 |6的数字学习循环:262 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 144 |接受| 0.12308 | 11.197 | 0.098462 | 0.10945 |集合|的方法:包| | |0 |1 |2 |3 |5 |6 NumLearningCycles: 262 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

| 145 |接受| 0.11692 | 0.098462 | 0.11056 |集合|方法:包| | |0 |1 |2 |3 |4 |5 bb16数字循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

| 146 |接受| 0.11692 | 8.728 | 0.098462 | 0.10939 |集合|方法:包| | |0 |1 |2 |3 |4 |5 bb16数字循环:201 |7 |8 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 4 |6

| 147 |接受| 0.11692 | 10.952 | 0.098462 | 0.11014 |集合|方法:包| | |0 |1 |2 |3 |5 |6数字学习循环:259 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 1 |6

|148 |接受|0.12 |8.5123 |0.098462 |0.10999 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |

| 149 |接受| 0.11077 | 10.694 | 0.098462 | 0.11039 |集合|方法:包| | |0 |1 |1 |3 |5 |6数字学习循环:257 |7 |8 |9 |9 |0 |1 |2 |3 |4 |5 MinLeafSize: 2 |6

|150 |接受|0.10769 |10.999 |0.098462 |0.10991 |合奏|方法：袋|| | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 1 |

__________________________________________________________优化完成。最大评价150个已达。总函数计算:150总运行时间:1758.9912秒。总目标函数评价时间:985.2563最佳观察可行点是一个模型:方法:袋NumLearningCycles: 201 MinLeafSize: 1观察目标函数值= 0.098462估计目标函数值= 0.11265时间评估函数= 9.241最好了,估计可行点(根据模型)是一个整体模型:方法:袋NumLearningCycles: 256 MinLeafSize: 1估计目标函数值= 0.10991估计时间评估函数= 11.0259

最后的模型返回fitcauto对应于最佳估计可行点。返回模型之前，使用整个训练数据（功能重新训练它carsTrain），上市学习者(或模型)类型，以及显示的超参数值。

评估测试集的性能

评估模型在测试集上的性能。

testAccuracy = 1  - 损失（MDL，carsTest，'起源')

testAccuracy = 0.9520

confusionchart（carsTest.Origin，预测（MDL，carsTest））

自动选择分类使用矩阵数据

这个示例使用:

开立真实脚本

采用fitcauto自动选择具有优化超参数的分类模型，将给定的预测器和响应数据存储在单独的变量中。

加载数据

加载humanactivity数据集。该数据集包含了24075个对五种人类身体活动的观察:坐(1)、站(2)、走(3)、跑(4)和跳舞(5)。每个观察有60个特征，提取自智能手机加速度计传感器测量的加速度数据。的变量功绩包含的60个特征为24075个观测预测数据矩阵，并响应变量ACTID包含观测为整数的活动标识。

加载humanactivity

分区数据

将数据划分为训练集和测试集。使用90%的观察值来选择模型，使用10%的观察值来验证所返回的最终模型fitcauto。采用cvpartition保留10%的观测值用于测试。

RNG（“默认”)%表示分区的重现性C = cvpartition（ACTID，“坚持”,0.10);trainingIndices =培训(c);训练集的%索引XTrain =技艺（trainingIndices，:);YTrain = ACTID（trainingIndices）;testIndices =测试(c);测试集的%索引XTest =壮举(testIndices:);欧美= actid (testIndices);

跑fitcauto

通过训练数据fitcauto。默认，fitcauto确定要尝试的适当模型(或学习者)类型，使用贝叶斯优化为这些模型找到良好的超参数值，并返回具有最佳预期性能的经过训练的模型。指定以并行方式运行优化(需要并行计算工具箱™)。返回第二个输出OptimizationResults其中包含贝叶斯优化的细节。

预计这个模型选择过程需要一些时间。

选择=结构(“UseParallel”,真正的);[铜牌，OptimizationResults] = fitcauto（XTrain，YTrain，“HyperparameterOptimizationOptions”、选择);

警告:在优化Naive Bayes 'Width'参数时，建议首先对所有数值谓词进行标准化。如果您这样做了，请忽略此警告。

开始使用“本地”轮廓平行池（parpool）......连接到并行池（工号：6）。复制目标函数，工人...完成复制目标函数的工人。

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 1 | 6 | Best | 0.28088 | 51.451 | 0.28088 | 0.28088 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.22686 | | | | | | | | | | KernelScale: 330.4 |

| 2 | 5 |接受| 0.036413 | 60.195 | 0.025845 | 0.11438 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | | MinLeafSize: 1786 | | | | | | | | | | MaxNumSplits: 12 | | 3 | 5 |最好| 0.025845 | 5.5008 | 0.025845 | 0.11438 | |树MinLeafSize: 59 |

| 4 | 6 |最佳| 0.017722 | 6.6481 | 0.017722 | 0.021702 |树| minleaf9 |

|5 |6 |接受|0.017722 |6.4868 |0.017722 |0.020592 |树|MinLeafSize：9 |

最好| 6 | 6 | | 0.0064611 | 79.739 | 0.0064611 | 0.020592 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 23 |

| 7 | 6 |接受| 0.050212 | 1.278 | 0.0064611 | 0.020592 | nb |分布名称:normal | |0 |1 |2 |3 |4 |5 |6 |7 |8 Width: NaN |9

| 8 | 6 |接受| 0.050212 | 1.2476 | 0.0064611 | 0.020592 | nb |分布名称:normal | |0 |1 |2 |3 |4 |5 |6 |7 |8 Width: NaN |9

|9 |6 |接受|0.02266 |189.95 |0.0064611 |0.020592 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 218 | | | | | | | | | | MinLeafSize: 2 | | | | | | | | | | MaxNumSplits: 63 |

|10 |6 |接受|0.033598 |203.62 |0.0064611 |0.020592 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 264 | | | | | | | | | | MinLeafSize: 7 | | | | | | | | | | MaxNumSplits: 36 |

| 11 | 6 |接受| 0.59166 | 28.752 | 0.0064611 | 0.020592 | nb |分布名称:内核| |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:4.7212e-14 |9

| 12 | 6 |接受| 0.04389 | 155.77 | 0.0064611 | 0.020592 |支持向量机|编码:onevsall | |0 |1 |3 |6 |6 |7 |8 BoxConstraint: 0.068467 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 117.01 |9

| 13 | 6 |接受| 0.021599 | 60.254 | 0.0064611 | 0.020592 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | | MinLeafSize: 1247 | | | | | | | | | | MaxNumSplits: 45 |

|14 |6 |接受|0.043567 |23.25 |0.0064611 |0.020592 |KNN |NumNeighbors：144 |

| 15 | 6 |接受| 0.028844 | 22.102 | 0.0064611 | 0.020592 | knn | NumNeighbors: 18 |

|16 |6 |接受|0.024598 |22.408 |0.0064611 |0.020592 |KNN |NumNeighbors：7 |

|17 |6 |接受|0.03009 |21.623 |0.0064611 |0.020592 |KNN |NumNeighbors：27 |

|18 |6 |接受|0.016891 |7.2949 |0.0064611 |0.019464 |树|MinLeafSize：2 |

|19 |6 |接受|0.040059 |4.3418 |0.0064611 |0.022968 |树|MinLeafSize：166 |

|20 |6 |接受|0.060319 |2.5459 |0.0064611 |0.023364 |树|MinLeafSize：1881 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 21 | 6 | Accept | 0.050212 | 1.1819 | 0.0064611 | 0.023364 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |

|22 |6 |接受|0.036552 |21.442 |0.0064611 |0.023364 |KNN |NumNeighbors：67 |

|23 |6 |接受|0.050212 |0.69192 |0.0064611 |0.023364 |NB |DistributionNames：正常| | | | | | | | | | Width: NaN |

|24 |6 |接受|0.11076 |37.67 |0.0064611 |0.023364 |KNN |NumNeighbors：2637 |

25 | | 6 |接受| 0.28711 | 69.473 | 0.0064611 | 0.030365 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 287 | | | | | | | | | | MinLeafSize: 4344 | | | | | | | | | | MaxNumSplits: 48 |

| 26 | 6 |接受| 0.58127 | 32.81 | 0.0064611 | 0.030365 | nb |的分布名称:内核| |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:1.6293e-06 |9

|27 |6 |接受|0.015784 |7.406 |0.0064611 |0.027375 |树|MinLeafSize：1 |

|28 |6 |接受|0.59166 |383.03 |0.0064611 |0.027375 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 790.4 | | | | | | | | | | KernelScale: 0.014348 |

|29 |6 |接受|0.069319 |1.9964 |0.0064611 |0.034633 |树|MinLeafSize：2284 |

|30 |6 |接受|0.043336 |3.5634 |0.0064611 |0.033826 |树|MinLeafSize：432 |

| 31 | 6 |接受| 0.10555 | 35.914 | 0.0064611 | 0.033826 | knn |数字邻居:2430 |

|32 |6 |接受|0.021183 |6.3954 |0.0064611 |0.021851 |树|MinLeafSize：17 |

|33 |6 |接受|0.050212 |1.098 |0.0064611 |0.021851 |NB |DistributionNames：正常| | | | | | | | | | Width: NaN |

|34 |6 |接受|0.014353 |62.709 |0.0064611 |0.021851 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 587 | | | | | | | | | | MaxNumSplits: 11 |

|35 |6 |接受|0.043428 |191.69 |0.0064611 |0.021851 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 288 | | | | | | | | | | MinLeafSize: 45 | | | | | | | | | | MaxNumSplits: 23 |

|36 |6 |接受|0.4226 |559.17 |0.0064611 |0.021851 |NB |DistributionNames：内核| | | | | | | | | | Width: 72.906 |

| | 6 | 37接受| 0.74165 | 25.64 | 0.0064611 | 0.021851 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | | MinLeafSize: 8856 | | | | | | | | | | MaxNumSplits: 36 |

| 38 | 6 |接受| 0.59166 | 27.996 | 0.0064611 | 0.021851 | nb |的分布名称:内核| |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:1.191e-07 |9

|39 |6 |最佳|0.0041074 |91.985 |0.0041074 |0.021851 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 129 | | | | | | | | | | MaxNumSplits: 100 |

| 40 | 6 |接受| 0.73985 | 542.35 | 0.0041074 | 0.021851 | nb |分布名称:kernel | |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:1055.8 |9

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 41 | 5 |接受| 0.57615 | 353.08 | 0.0041074 | 0.021851 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 2.2347 | | | | | | | | | | KernelScale: 0.16176 | | 42 | 5 |接受| 0.087087 | 32.51 | 0.0041074 | 0.021851 |资讯| NumNeighbors: 1634 |

| 43 | 6 |接受| 0.050212 | 0.85611 | 0.0041074 | 0.021851 | nb |分布名称:normal | |0 |1 |2 |3 |4 |5 |6 |7 |8 Width: NaN |9

| 44 | 6 |接受| 0.050212 | 0.75838 | 0.0041074 | 0.021851 | nb |分布名称:正常| |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:NaN |9

|45 |6 |接受|0.025891 |21.86 |0.0041074 |0.021851 |KNN |NumNeighbors：6 |

46 | | 6 |接受| 0.030414 | 162.3 | 0.0041074 | 0.021851 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 39 |

|47 |6 |接受|0.03286 |178.49 |0.0041074 |0.021851 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 73 | | | | | | | | | | MaxNumSplits: 43 |

|48 |6 |接受|0.037244 |156.66 |0.0041074 |0.021851 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 5.9571 | | | | | | | | | | KernelScale: 840.87 |

|49 |6 |接受|0.01703 |7.0967 |0.0041074 |0.020426 |树|MinLeafSize：4 |

| 50 | 6 |接受| 0.017168 | 6.8667 | 0.0041074 | 0.0183 |树| MinLeafSize: 5 |

51 | | 6 |接受| 0.039321 | 221.28 | 0.0041074 | 0.0183 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 299 | | | | | | | | | | MinLeafSize: 31日| | | | | | | | | | MaxNumSplits: 25 |

| 52 | 6 |接受| 0.046474 | 199.35 | 0.0041074 | 0.0183 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 2.9119 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 12.771 |9

|53 |6 |接受|0.59166 |433.93 |0.0041074 |0.0183 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 92.735 | | | | | | | | | | KernelScale: 0.0010711 |

| 54 | 6 |接受| 0.054135 | 3.7916 | 0.0041074 | 0.018419 |树| MinLeafSize: 783 |

| 55 | 6 |接受| 0.049797 | 27.695 | 0.0041074 | 0.018419 | knn |数字邻居:331 |

| 56 | 6 |接受| 0.046566 | 26.856 | 0.0041074 | 0.018419 | knn | NumNeighbors: 193 |

57 | | 6 |接受| 0.049197 | 188.34 | 0.0041074 | 0.018419 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 253 | | | | | | | | | | MinLeafSize: 14 | | | | | | | | | | MaxNumSplits: 22 |

| 58 | 6 |接受| 0.022706 | 25.765 | 0.0041074 | 0.018419 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 1.3505 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 127.55 |9

|59 |6 |接受|0.028798 |5.7904 |0.0041074 |0.018856 |树|MinLeafSize：84 |

|60 |6 |接受|0.041351 |29.766 |0.0041074 |0.018856 |KNN |NumNeighbors：124 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 61 | |接受| 0.030044 | 28.239 | 0.0041074 | 0.018856 |资讯| NumNeighbors: 26 |

| 62 | 6 |接受| 0.11838 | 295.65 | 0.0041074 | 0.018856 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0027874 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 33.944 |9

| 63 | 6 |接受| 0.47116 | 55.67 | 0.0041074 | 0.018856 | nb |分布名称:kernel | |0 |1 |2 |3 |4 |5 |6 |7 |8 Width: 4.7553e-05 |9

| 64 | 6 |接受| 0.26574 | 104.02 | 0.0041074 | 0.018856 | nb |分布名称:kernel | |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:0.0011441 |9

|65 |6 |接受|0.050212 |0.69002 |0.0041074 |0.018856 |NB |DistributionNames：正常| | | | | | | | | | Width: NaN |

|66 |6 |接受|0.017168 |6.7802 |0.0041074 |0.018674 |树|MinLeafSize：5 |

| 67 | 6 |接受| 0.077072 | 281.55 | 0.0041074 | 0.018674 | nb |分布名称:kernel | |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:0.026902 |9

|68 |6 |接受|0.031613 |26.638 |0.0041074 |0.018674 |KNN |NumNeighbors：33 |

|69 |6 |接受|0.02003 |108.02 |0.0041074 |0.018674 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 609.47 | | | | | | | | | | KernelScale: 39.88 |

|70 |6 |接受|0.14141 |92.623 |0.0041074 |0.018674 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 292 | | | | | | | | | | MinLeafSize: 3870 | | | | | | | | | | MaxNumSplits: 33 |

|71 |6 |接受|0.01103 |74.107 |0.0041074 |0.018674 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 203 | | | | | | | | | | MaxNumSplits: 14 |

|72 |6 |接受|0.0093225 |78.987 |0.0041074 |0.018674 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 225 | | | | | | | | | | MinLeafSize: 461 | | | | | | | | | | MaxNumSplits: 45 |

| 73 | 6 |接受| 0.0081226 | 75.993 | 0.0041074 | 0.018674 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 212 | | | | | | | | | | MinLeafSize: 224 | | | | | | | | | | MaxNumSplits: 19 |

|74 |6 |接受|0.0086302 |75.677 |0.0041074 |0.011486 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 372 | | | | | | | | | | MaxNumSplits: 66 |

| 75 | 6 |接受| 0.012461 | 69.727 | 0.0041074 | 0.0099707 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 219 | | | | | | | | | | MinLeafSize: 602 | | | | | | | | | | MaxNumSplits: 16 |

|76 |6 |接受|0.011907 |66.364 |0.0041074 |0.018674 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 535 | | | | | | | | | | MaxNumSplits: 17 |

|77 |6 |接受|0.066227 |125.89 |0.0041074 |0.0096155 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 579 | | | | | | | | | | MaxNumSplits: 44 |

|78 |6 |接受|0.014168 |64.533 |0.0041074 |0.018674 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 670 | | | | | | | | | | MaxNumSplits: 21 |

| 79 | 6 |接受| 0.012276 | 73.034 | 0.0041074 | 0.0096795 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 228 | | | | | | | | | | MinLeafSize: 614 | | | | | | | | | | MaxNumSplits: 94 |

| 80 | 6 |接受| 0.017353 | 61.294 | 0.0041074 | 0.0094804 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 204 | | | | | | | | | | MinLeafSize: 772 | | | | | | | | | | MaxNumSplits: 13 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 81 | |接受| 0.01846 | 61.652 | 0.0041074 | 0.0084959 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 211 | | | | | | | | | | MinLeafSize: 879 | | | | | | | | | | MaxNumSplits: 79 |

| 82 | 6 |接受| 0.016937 | 67.027 | 0.0041074 | 0.0092617 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 805 | | | | | | | | | | MaxNumSplits: 20 |

| 83 | 6 |接受| 0.012876 | 256.48 | 0.0041074 | 0.0092617 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 501.55 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 190.02 |9

| 84 | 6 |接受| 0.017537 | 61.538 | 0.0041074 | 0.0091728 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 836 | | | | | | | | | | MaxNumSplits: 40 |

| 85 | 6 |接受| 0.01463 | 43.6 | 0.0041074 | 0.0091728 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 397.97 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 310.04 |9

|86 |6 |接受|0.025522 |24.217 |0.0041074 |0.0091728 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 36.55 | | | | | | | | | | KernelScale: 658.03 |

| 87 | 6 |接受| 0.01703 | 68.966 | 0.0041074 | 0.0092732 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 230 | | | | | | | | | | MinLeafSize: 822 | | | | | | | | | | MaxNumSplits: 22 |

| 88 | 6 |接受| 0.021137 | 114.19 | 0.0041074 | 92732 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 593.64 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 37.983 |9

|89 |6 |接受|0.59166 |2089.7 |0.0041074 |0.0092732 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 0.045413 | | | | | | | | | | KernelScale: 0.0034709 |

| 90 | 6 |接受| 0.019799 | 57.157 | 0.0041074 | 0.0089505 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 202 | | | | | | | | | | MinLeafSize: 962 | | | | | | | | | | MaxNumSplits: 81 |

| 91 | 6 |接受| 0.085472 | 118.41 | 0.0041074 | 8.9505 |支持向量机|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 BoxConstraint: 855.69 |9 |0 |1 |2 |3 |4 |6 |7 |8 KernelScale: 6.5486 |9

|92 |6 |接受|0.02003 |59.729 |0.0041074 |0.0095371 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 216 | | | | | | | | | | MinLeafSize: 1004 | | | | | | | | | | MaxNumSplits: 12 |

| 93 | 6 |接受| 0.019845 | 58.677 | 0.0041074 | 0.0095584 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 208 | | | | | | | | | | MinLeafSize: 926 | | | | | | | | | | MaxNumSplits: 32 |

| 94 | 6 |接受| 0.021506 | 59.315 | 0.0041074 | 0.011473 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 216 | | | | | | | | | | MinLeafSize: 1063 | | | | | | | | | | MaxNumSplits: 82 |

|95 |6 |接受|0.022383 |55.893 |0.0041074 |0.0091004 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 205 | | | | | | | | | | MinLeafSize: 1150 | | | | | | | | | | MaxNumSplits: 11 |

|96 |6 |接受|0.054966 |273.69 |0.0041074 |0.0091004 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 550 | | | | | | | | | | KernelScale: 10.56 |

|97 |6 |接受|0.021322 |64.607 |0.0041074 |0.0089091 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 229 | | | | | | | | | | MinLeafSize: 1111 | | | | | | | | | | MaxNumSplits: 52 |

| 98 | 6 |接受| 0.021691 | 60.128 | 0.0041074 | 0.0083855 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 1112 | | | | | | | | | | MaxNumSplits: 14 |

| 99 | 6 |接受| 0.59166 | 2658.6 | 0.0041074 | 0.0083855 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.98129 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.0054937 |9

|100 |6 |接受|0.22499 |820.13 |0.0041074 |0.0083855 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 869.37 | | | | | | | | | | KernelScale: 1.5553 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 101 | |接受| 0.021691 | 63.07 | 0.0041074 | 0.008821 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 228 | | | | | | | | | | MinLeafSize: 1180 | | | | | | | | | | MaxNumSplits: 83 |

|102 |6 |接受|0.02206 |57.161 |0.0041074 |0.0095876 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 1208 | | | | | | | | | | MaxNumSplits: 17 |

| 103 | 6 |接受| 0.022014 | 59.267 | 0.0041074 | 0.0094147 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | | MinLeafSize: 1187 | | | | | | | | | | MaxNumSplits: 61 |

|104 |6 |接受|0.23763 |216.82 |0.0041074 |0.0094147 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 512.6 | | | | | | | | | | KernelScale: 1.2736 |

|105 |6 |接受|0.050212 |0.76452 |0.0041074 |0.0094147 |NB |DistributionNames：正常| | | | | | | | | | Width: NaN |

| 106 | 6 |接受| 0.023352 | 259.58 | 0.0041074 | 0.010382 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 220 | | | | | | | | | | MinLeafSize: 1372 | | | | | | | | | | MaxNumSplits: 96 |

| 107 | 6 |接受| 0.022983 | 60.428 | 0.0041074 | 0.0092473 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 219 | | | | | | | | | | MinLeafSize: 1293 | | | | | | | | | | MaxNumSplits: 25 |

|108 |6 |接受|0.022568 |62.238 |0.0041074 |0.0094314 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 231 | | | | | | | | | | MinLeafSize: 1344 | | | | | | | | | | MaxNumSplits: 19 |

|109 |6 |接受|0.025383 |59.558 |0.0041074 |0.0091873 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 222 | | | | | | | | | | MinLeafSize: 1404 | | | | | | | | | | MaxNumSplits: 12 |

|110 |6 |接受|0.022522 |65.564 |0.0041074 |0.009405 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | | MinLeafSize: 1360 | | | | | | | | | | MaxNumSplits: 14 |

|111 |6 |接受|0.022799 |61.264 |0.0041074 |0.0091171 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 226 | | | | | | | | | | MinLeafSize: 1356 | | | | | | | | | | MaxNumSplits: 90 |

|112 |6 |接受|0.058935 |45.569 |0.0041074 |0.0096787 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 2521 | | | | | | | | | | MaxNumSplits: 33 |

|113 |6 |接受|0.022937 |64.08 |0.0041074 |0.0087354 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 231 | | | | | | | | | | MinLeafSize: 1383 | | | | | | | | | | MaxNumSplits: 88 |

|114 |6 |接受|0.027783 |59.671 |0.0041074 |0.012899 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 224 | | | | | | | | | | MinLeafSize: 1551 | | | | | | | | | | MaxNumSplits: 20 |

| 115 | 6 |接受| 0.027737 | 53.366 | 0.0041074 | 0.0096795 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 203 | | | | | | | | | | MinLeafSize: 1548 | | | | | | | | | | MaxNumSplits: 11 |

|116 |6 |接受|0.027137 |62.9 |0.0041074 |0.009529 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 1479 | | | | | | | | | | MaxNumSplits: 10 |

|117 |6 |接受|0.59166 |3642 |0.0041074 |0.009529 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 658.37 | | | | | | | | | | KernelScale: 0.0016161 |

| 118 | 6 |接受| 0.23163 | 1370.3 | 0.0041074 | 0.009529 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 720.53 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 1.1039 |9

| 119 | 6 |接受| 0.027183 | 58.796 | 0.0041074 | 0.0097206 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 223 | | | | | | | | | | MinLeafSize: 1544 | | | | | | | | | | MaxNumSplits: 25 |

|120 |6 |接受|0.027091 |62.912 |0.0041074 |0.0086543 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 238 | | | | | | | | | | MinLeafSize: 1499 | | | | | | | | | | MaxNumSplits: 42 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 121 | 6 | Accept | 0.59166 | 3676.1 | 0.0041074 | 0.0086543 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 17.982 | | | | | | | | | | KernelScale: 0.0043756 |

|122 |6 |接受|0.027783 |56.988 |0.0041074 |0.0095236 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 225 | | | | | | | | | | MinLeafSize: 1551 | | | | | | | | | | MaxNumSplits: 38 |

| 123 | 6 |接受| 0.22896 | 1222.6 | 0.0041074 | 0.0095236 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 30.726 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 1.2194 |9

|124 |6 |接受|0.028706 |53.614 |0.0041074 |0.0094105 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 1571 | | | | | | | | | | MaxNumSplits: 64 |

| 125 | 6 |接受| 0.026906 | 68.419 | 0.0041074 | 0.0096214 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | | MinLeafSize: 1548 | | | | | | | | | | MaxNumSplits: 72 |

| 126 | 6 |接受| 0.040705 | 51.812 | 0.0041074 | 0.0096155 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 2028 | | | | | | | | | | MaxNumSplits: 12 |

| 127 | 6 |接受| 0.10901 | 491.59 | 0.0041074 | 0.0096155 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 317.07 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 3.345 |9

|128 |6 |接受|0.038813 |50.737 |0.0041074 |0.0094473 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | | MinLeafSize: 1857 | | | | | | | | | | MaxNumSplits: 77 |

|129 |6 |接受|0.04269 |56.006 |0.0041074 |0.01155 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 250 | | | | | | | | | | MinLeafSize: 2192 | | | | | | | | | | MaxNumSplits: 17 |

|130 |6 |最佳|0.0040151 |144.99 |0.0040151 |0.0076249 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 286 | | | | | | | | | | MinLeafSize: 133 | | | | | | | | | | MaxNumSplits: 91 |

| 131 | 6 |接受| 0.039505 | 54.552 | 0.0040151 | 0.0093087 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 2016 | | | | | | | | | | MaxNumSplits: 15 |

|132 |6 |接受|0.039921 |49.255 |0.0040151 |0.0077926 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | | MinLeafSize: 1997 | | | | | | | | | | MaxNumSplits: 77 |

|133 |6 |接受|0.048182 |48.846 |0.0040151 |0.0086785 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 2243 | | | | | | | | | | MaxNumSplits: 13 |

| 134 | 6 |接受| 0.041859 | 66.081 | 0.0040151 | 0.0079912 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 263 | | | | | | | | | | MinLeafSize: 2099 | | | | | | | | | | MaxNumSplits: 32 |

|135 |6 |接受|0.046105 |68.865 |0.0040151 |0.0078968 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 287 | | | | | | | | | | MinLeafSize: 2342 | | | | | | | | | | MaxNumSplits: 12 |

|136 |6 |接受|0.0042459 |150.03 |0.0040151 |0.008766 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 297 | | | | | | | | | | MinLeafSize: 163 | | | | | | | | | | MaxNumSplits: 92 |

| 137 | 6 |接受| 0.046289 | 62.375 | 0.0040151 | 0.012639 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 273 | | | | | | | | | | MinLeafSize: 2260 | | | | | | | | | | MaxNumSplits: 67 |

| 138 | 6 |接受| 0.044028 | 53.269 | 0.0040151 | 0.0092674 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 224 | | | | | | | | | | MinLeafSize: 2190 | | | | | | | | | | MaxNumSplits: 12 |

|139 |6 |接受|0.04892 |45.442 |0.0040151 |0.006892 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 2354 | | | | | | | | | | MaxNumSplits: 17 |

|140 |6 |接受|0.11515 |99.696 |0.0040151 |0.0093722 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 281 | | | | | | | | | | MinLeafSize: 2297 | | | | | | | | | | MaxNumSplits: 30 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 141 | |接受| 0.10845 | 91.18 | 0.0040151 | 0.0065827 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | | MinLeafSize: 2275 | | | | | | | | | | MaxNumSplits: 37 |

|142 |6 |接受|0.74165 |34.944 |0.0040151 |0.0072537 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 253 | | | | | | | | | | MinLeafSize: 8804 | | | | | | | | | | MaxNumSplits: 11 |

最好| 143 | 6 | | 0.0038767 | 158.75 | 0.0038767 | 0.0057394 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 294 | | | | | | | | | | MinLeafSize: 106 | | | | | | | | | | MaxNumSplits: 94 |

| 144 | 6 |接受| 0.04592 | 64.846 | 0.0038767 | 0.0062301 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 290 | | | | | | | | | | MinLeafSize: 2326 | | | | | | | | | | MaxNumSplits: 34 |

| 145 | 6 |接受| 0.047074 | 55.822 | 0.0038767 | 0.0062084 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | | MinLeafSize: 2345 | | | | | | | | | | MaxNumSplits: 60 |

|146 |6 |接受|0.04832 |50.283 |0.0038767 |0.0092604 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 2350 | | | | | | | | | | MaxNumSplits: 19 |

| 147 | 6 |接受| 0.016107 | 26.377 | 0.0038767 | 92604 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 94.982 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 222.61 |9

| 148 | 6 |接受| 0.050166 | 53.779 | 0.0038767 | 0.0068092 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | | MinLeafSize: 2407 | | | | | | | | | | MaxNumSplits: 20 |

| 149 | 6 |接受| 0.058196 | 46.596 | 0.0038767 | 0.0063746 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 2469 | | | | | | | | | | MaxNumSplits: 17 |

|150 |6 |接受|0.067519 |48.158 |0.0038767 |0.0065889 |合奏|方法：AdaBoostM2 | | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | | MinLeafSize: 3054 | | | | | | | | | | MaxNumSplits: 68 |

__________________________________________________________优化完成。最大评价150个已达。总功能评价：150总经过时间：5515.7636秒。总目标函数评估时间：27568.0193最佳观察到的可行点是用一个整体模型：方法：AdaBoostM2 NumLearningCycles：294 MinLeafSize：106个MaxNumSplits：94观察到的目标函数值= 0.0038767估计目标函数值= 0.006912功能评估时间= 158.7495最佳估计可行点（根据模型）是具有一个整体模型：方法：AdaBoostM2 NumLearningCycles：210 MinLeafSize：129个MaxNumSplits：100估计目标函数值= 0.0065889估计函数评估时间= 129.4444

最后的模型返回fitcauto对应于最佳估计可行点。返回模型之前，使用整个训练数据（功能重新训练它XTrain和YTrain），上市学习者(或模型)类型，以及显示的超参数值。

评估测试集的性能

评估最终模型在测试数据集上的性能。

testAccuracy = 1  - 损失（MDL，XTEST，YTest）

testAccuracy = 0.9963

最终的模型正确分类的意见超过99％。

结合特征选择与自动分类选择

这个示例使用:

开立真实脚本

采用fitcauto自动地选择具有优化的超参数，特定的预测和响应数据存储在一个表中的分类模型。将数据传递到前fitcauto，执行特征选择以从数据集中移除不重要的预测器。

加载和分区数据

读取示例文件CreditRating_Historical.dat一个表中。预测数据包括企业客户的财务比率和行业信息。响应变量由评级机构分配的信用评级组成。预览数据集的前几行。

企业资信= readtable（“CreditRating_Historical.dat”);头（企业资信）

ans =8×8表ID WC_TA RE_TA EBIT_TA MVE_BVTD S_TA行业评级_____持续累积________ _____ ________ 62394 0.013 0.104 0.036 0.447 0.142 3 {“BB”} 48608 0.232 0.335 0.062 1.969 0.281 8 {A} 42444 0.311 0.367 0.074 1.935 0.366 1 {A} 48631 0.194 0.263 0.062 1.017 0.228 - 4 {BBB的}43768 0.121 0.413 0.057 3.647 0.466 12 {' AAA '} 39255 -0.117 -0.799 0.01 0.179 0.082 - 4 {“CCC”} 62236 0.087 0.158 0.049 0.816 0.324 - 2 {BBB的}39354 0.005 0.181 0.034 2.597 0.388 7 {“AA”}

因为。中的每个值ID变量是一个独特的客户ID，也就是长度（唯一的（creditrating.ID））等于观察到的个数信用评级中，ID变量是一个差预测值。去除ID从表变量，并转换行业变量,分类变量。

creditrating = removevars (creditrating,'ID');creditrating。行业= categorical(creditrating.Industry);

将数据划分为训练集和测试集。使用的模型选择和超参数调整过程的观察约85％，而观察的15％，以测试返回的最终模型的性能fitcauto新的数据。采用cvpartition对数据进行分区。

RNG（“默认”)%表示分区的重现性c = cvpartition (creditrating.Rating“坚持”,0.15);trainingIndices =培训(c);训练集的%索引testIndices =测试(c);测试集的%索引creditTrain =企业资信（trainingIndices，:);creditTest =企业资信（testIndices，:);

进行特征选择

在传递训练数据之前fitcauto，发现使用的重要预测指标fscchi2函数。通过可视化的预测分数酒吧函数。因为有些分数可以正和酒吧丢弃正值，首先绘制有限的分数，然后绘制有限的表示正分数用不同的颜色表示。

[IDX，分数] = fscchi2（creditTrain，'评分');栏(分数(idx))%表示有限分数持有在重要= isinf(分数);finiteMax = max(分数(~重要));酒吧(finiteMax *重要(idx))%表示Inf得分持有离xticklabels (strrep (creditTrain.Properties.VariableNames (idx),“_”,“\ _”））xtickangle（45）图例（{“有限分数”,“正分数”})

请注意,行业预测器具有对应于一个低分p- 值是大于0.05，这表明行业可能不是一个重要特征。去除行业从训练和测试数据集功能。

creditTrain = removevars (creditTrain,'行业');creditTest = removevars（creditTest，'行业');

跑fitcauto

fitcauto不支持线性或内核分万博1manbetx类模型的表。包括这些模型（或学习者）的类型，训练数据传递到转换之前预测数据以矩阵的形式fitcauto。转换的测试数据为好。这种转换是可能的，因为所有剩余的预测是数字。

predictorTrain = removevars（creditTrain，'评分');XTrain = table2array（predictorTrain）;YTrain = creditTrain.Rating;predictorTest = removevars（creditTest，'评分');XTest = table2array (predictorTest);欧美= creditTest.Rating;

通过训练数据fitcauto。该函数使用贝叶斯优化来选择模型及其超参数值，并返回一个经过训练的模型MDL具有最佳的预期性能。指定尝试所有可用的学习器类型并并行运行优化(需要并行计算工具箱™)。返回第二个输出结果其中包含贝叶斯优化的细节。

预计这一过程需要一段时间。

选择=结构(“UseParallel”,真正的);[Mdl,结果]= fitcauto (XTrain YTrain,...“学习者”,'所有',“HyperparameterOptimizationOptions”、选择);

警告:在优化Naive Bayes 'Width'参数时，建议首先对所有数值谓词进行标准化。如果您这样做了，请忽略此警告。

开始使用“本地”轮廓平行池（parpool）......连接到并行池（工号：6）。复制目标函数，工人...完成复制目标函数的工人。

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 1 | 6 |的| 0.42716 | 2.8649 | 0.42716 | 0.42716 | discr |三角洲:0.00046441 | | | | | | | | | | Gamma: 0.2485 |0

| 2 | 4 |接受| 0.74185 | 5.0967 | 0.24948 | 0.42911 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.48455 | | | | | | | | | | KernelScale: 354.44 | | 3 | 4 |的| 0.24948 | 4.9586 | 0.24948 | 0.42911 | |线性编码:onevsone | | | | | | | | | |λ:6.3551 e-08 | | | | | | | | | |学习者:物流| | 4 | 4 |接受| 0.29794 | 3.246 | 0.24948 | 0.42911 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 12 | | | | | | | | | | LearnRate:0.063776 | | | | | | | | | | MinLeafSize: 277 |0

最好| 5 | 3 | | 0.24708 | 9.215 | 0.24708 | 0.36903 | |内核编码:onevsone | | | | | | | | | | KernelScale: 7.8433 | | | | | | | | | |λ:1.4468 e-06 | | 6 | 3 |接受| 0.25067 | 0.65429 | 0.24708 | 0.36903 |资讯| NumNeighbors: 105 | | | | | | | | | |距离:闵可夫斯基|

|7 |6 |接受|0.52917 |2.9984 |0.24708 |0.63551 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 0.002417 | | | | | | | | | | KernelScale: 356.9 |

| 8 | 3 |接受| 0.55818 | 0.61959 | 0.24708 | 0.58763 | discrδ:| 0.98612 | | | | | | | | | |γ:0.86519 | | 9 | 3 |接受| 0.3781 | 1.7268 | 0.24708 | 0.58763 | |线性编码:onevsall | | | | | | | | | |λ:1.0412 e-06 | | | | | | | | | |学习者:物流| | 10 | 3 |接受| 0.43225 | 0.73455 | 0.24708 | 0.58763 | discrδ:| 0.00013711 | | | | | | | | | |γ:0.60585 | | 11 | 3 |接受| 0.47712 | 4.1471 | 0.24708 | 0.58763 |支持向量机|编码:| | | | | | | | | | BoxConstraint: 2.7347 |0 |1 |2 |3 |4 |5 |6 |7 |8 |9 KernelScale: 24.465 |0

| 12 | 6 |接受| 0.25695 | 2.4614 | 0.24708 | 0.58763 | nb |分布名称:kernel | |0 |1 |2 |3 |4 |5 |6 |7 |8宽度:0.057566 |9

| | 3 | 13日接受| 0.26383 | 0.54745 | 0.24379 | 0.58763 | |树MinLeafSize: 30 | | 14 | 3 |接受| 0.42327 | 0.84715 | 0.24379 | 0.58763 |资讯| NumNeighbors: 56 | | | | | | | | | |距离:余弦| |最好15 | 3 | | 0.24379 | 2.0052 | 0.24379 | 0.58763 | |线性编码:onevsone | | | | | | | | | |λ:5.9172 e-05 | | | | | | | | | |学习者:支持向量机| | 16 | 3 |接受| 0.82112 | 5.0159 | 0.24379 | 0.58763 | |内核编码:onevsall | | | | | | | | | | KernelScale:0.0043375 | | | | | | | | | | Lambda: 0.0023789 |0

|17 |6 |接受|0.45169 |1.0941 |0.24379 |0.32154 |线性|编码：onevsall | | | | | | | | | | Lambda: 0.0028505 | | | | | | | | | | Learner: svm |

18岁| | 3 |接受| 0.53365 | 2.9722 | 0.24379 | 0.31096 |支持向量机|编码:onevsall | | | | | | | | | | BoxConstraint: 0.0022255 | | | | | | | | | | KernelScale: 206.47 | | | 3 | 19日接受| 0.74185 | 0.69997 | 0.24379 | 0.31096 | discr |三角洲:788.29 | | | | | | | | | |γ:0.096315 | | 20 | 3 |接受| 0.39186 | 3.7201 | 0.24379 | 0.31096 | |内核编码:onevsall | | | | | | | | | | KernelScale: 22.787 | | | | | | | | | |λ:4.7789 e-05 | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | 3 | 21日接受| 0.24439 | 1.7527 | 0.24379 | 0.31096 | |线性编码:onevsone | | | | | | | | | |λ:1.9056 e-08 | | | | | | | | | |学习者:支持向量机|

| 22 | 6 |接受| 0.4834 | 0.42353 | 0.24379 | 0.31096 | knn | NumNeighbors: 72 | |0 |1 |2 |3 |4 |5 |6 |7 |8距离:相关|9

|23 |3 |接受|0.74185 |0.12006 |0.24379 |0.31096 |树|MinLeafSize：1558 | | 24 | 3 | Accept | 0.27042 | 0.67315 | 0.24379 | 0.31096 | tree | MinLeafSize: 76 | | 25 | 3 | Accept | 0.45887 | 4.6976 | 0.24379 | 0.31096 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 7.1247 | | | | | | | | | | KernelScale: 0.9781 | | 26 | 3 | Accept | 0.28208 | 0.88675 | 0.24379 | 0.31096 | knn | NumNeighbors: 291 | | | | | | | | | | Distance: minkowski |

|27 |6 |接受|0.43255 |0.13008 |0.24379 |0.31096 |DISCR |三角洲：0.016844 | | | | | | | | | | Gamma: 0.64466 |

|28 |4 |接受|0.66796 |0.25188 |0.24379 |0.31096 |KNN |NumNeighbors：77 | | | | | | | | | | Distance: jaccard | | 29 | 4 | Accept | 0.28059 | 0.44707 | 0.24379 | 0.31096 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 30 | 4 | Accept | 0.65869 | 0.38657 | 0.24379 | 0.31096 | knn | NumNeighbors: 61 | | | | | | | | | | Distance: jaccard |

|31 |3 |接受|0.74185 |6.6038 |0.24379 |0.31096 |内核|编码：onevsone | | | | | | | | | | KernelScale: 0.0010962 | | | | | | | | | | Lambda: 0.035691 | | 32 | 3 | Accept | 0.27789 | 0.11562 | 0.24379 | 0.31096 | tree | MinLeafSize: 94 |

| 33 | 6 |接受| 0.74185 | 0.096361 | 0.24379 | 0.31096 | discr | Delta: 244.12 | |0 |1 |2 |3 |4 |5 |6 |7 |8 Gamma: 0.23748 |9

|34 |3 |接受|0.32456 |0.18499 |0.24349 |0.31096 |树|MinLeafSize：3 | | 35 | 3 | Accept | 0.63506 | 2.4392 | 0.24349 | 0.31096 | knn | NumNeighbors: 1563 | | | | | | | | | | Distance: mahalanobis | | 36 | 3 | Best | 0.24349 | 2.1919 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.044076 | | | | | | | | | | KernelScale: 0.035497 | | 37 | 3 | Accept | 0.6216 | 2.1779 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.055096 | | | | | | | | | | KernelScale: 6.2342 |

| 38 | 6 |接受| 0.42208 | 0.1616 | 0.24349 | 0.31096 | discr | Delta: 0.0090118 | |0 |1 |2 |3 |4 |5 |6 |7 |8 Gamma: 0.062207 |9

|39 |4 |接受|0.47173 |3.2018 |0.24349 |0.31096 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 3.7197 | | | | | | | | | | KernelScale: 2.9509 | | 40 | 4 | Accept | 0.74185 | 2.4165 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.019393 | | | | | | | | | | KernelScale: 332.27 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 41 | 4 | Accept | 0.53126 | 2.5238 | 0.24349 | 0.31096 | knn | NumNeighbors: 372 | | | | | | | | | | Distance: mahalanobis |

42 | | 4 |接受| 0.25965 | 15.125 | 0.24349 | 0.27803 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 150 | | | | | | | | | | LearnRate: 0.014842 | | | | | | | | | | MinLeafSize: 21 |

| 43 | 6 |接受| 0.74185 | 4.4109 | 0.24349 | 0.27803 |核心|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 KernelScale: 24.681 |9 |0 |1 |2 |3 |4 |6 |8 Lambda: 0.092669 |9

44 | | 3 |接受| 0.26413 | 22.354 | 0.24349 | 0.27803 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 304 | | | | | | | | | | LearnRate:南| | | | | | | | | | MinLeafSize: 100 | | 45 | 3 |接受| 0.24499 | 14.917 | 0.24349 | 0.27803 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.019387 | | | | | | | | | | KernelScale: 0.0047515 | | 46 | 3 |接受| 0.74185 | 4.3649 | 0.24349 | 0.27803 | |内核编码:onevsone | | | | | | | | | | KernelScale:24.681 | | | | | | | | | |λ:0.092669 | | 47 | 3 |接受| 0.74185 | 7.1585 | 0.24349 | 0.27803 | |内核编码:onevsone | | | | | | | | | | KernelScale: 24.681 | | | | | | | | | |λ:0.092669 |

|48 |6 |接受|0.28059 |0.18491 |0.24349 |0.27255 |NB |DistributionNames：正常| | | | | | | | | | Width: NaN |

|49 |3 |接受|0.60754 |3.9106 |0.24349 |0.27255 |内核|编码：onevsall | | | | | | | | | | KernelScale: 176.2 | | | | | | | | | | Lambda: 2.3903e-06 | | 50 | 3 | Accept | 0.42507 | 0.11067 | 0.24349 | 0.27255 | discr | Delta: 0.25925 | | | | | | | | | | Gamma: 0.82918 | | 51 | 3 | Accept | 0.28806 | 4.3596 | 0.24349 | 0.27255 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 42 | | | | | | | | | | LearnRate: 0.0031729 | | | | | | | | | | MinLeafSize: 33 | | 52 | 3 | Accept | 0.47024 | 3.0918 | 0.24349 | 0.27255 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0028932 | | | | | | | | | | KernelScale: 0.47789 |

| 53 | 6 |接受| 0.49477 | 3.6628 | 0.24349 | 0.27255 |核心|编码:onevsall | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 0.093586 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0050756 |9

54 | | 4 |接受| 0.27131 | 0.15291 | 0.24349 | 0.27548 | |树MinLeafSize: 20 | | | 4 |接受55 | 0.28059 | 0.46186 | 0.24349 | 0.27548 | nb | DistributionNames:正常| | | | | | | | | |宽度:南| | 56 | 4 |接受| 0.28059 | 0.45079 | 0.24349 | 0.27548 | nb | DistributionNames:正常| | | | | | | | | |宽度:南|

|57 |4 |接受|0.32456 |0.17695 |0.24349 |0.27548 |树|MinLeafSize：3 |

|58 |4 |接受|0.33144 |1.2562 |0.24349 |0.2843 |NB |DistributionNames：内核| | | | | | | | | | Width: 0.0020049 |

59 | | 3 |接受| 0.43314 | 23.917 | 0.24349 | 0.28235 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 357 | | | | | | | | | | LearnRate: 0.035928 | | | | | | | | | | MinLeafSize: 799 | | 60 | 3 |接受| 0.28059 | 0.13198 | 0.24349 | 0.28235 | nb | DistributionNames:正常| | | | | | | | | |宽度:南|

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 61 | 6 | Accept | 0.26234 | 0.55057 | 0.24349 | 0.28235 | knn | NumNeighbors: 157 | | | | | | | | | | Distance: euclidean |

|62 |4 |接受|0.31917 |10.817 |0.24349 |0.28235 |合奏|方法：RUSBoost | | | | | | | | | | NumLearningCycles: 132 | | | | | | | | | | LearnRate: 0.0014516 | | | | | | | | | | MinLeafSize: 104 | | 63 | 4 | Accept | 0.24529 | 5.709 | 0.24349 | 0.28235 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 4.6933 | | | | | | | | | | Lambda: 9.8945e-07 | | 64 | 4 | Accept | 0.43255 | 0.74626 | 0.24349 | 0.28235 | linear | Coding: onevsall | | | | | | | | | | Lambda: 2.7304e-07 | | | | | | | | | | Learner: svm |

| 65 | 4 |接受| 0.57972 | 2.8035 | 0.24349 | 0.28349 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.12255 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 81.172 |9

| 66 | 4 |接受| 0.26383 | 0.13419 | 0.24349 | 0.28235 | knn | NumNeighbors: 13 | |0 |1 |2 |3 |4 |5 |6 |7 |8 Distance: chebychev |9

| 67 | 3 |接受| 0.24469 | 17.277 | 0.24349 | 0.28235 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.062223 | | | | | | | | | | KernelScale: 0.0077043 | | 68 | |接受| 0.42596 | 0.095102 | 0.24349 | 0.28235 | discr |三角洲:9.4222 e-06 | | | | | | | | | |γ:0.15603 |

| 69 | 6 |接受| 0.67783 | 0.67221 | 0.28349 | 0.28349 |线性|编码:onevsall | |0 |1 |2 |3 |5 |6 |7 |8 Lambda: 2.4732 |9 |0 |1 |2 |3 |4 |6 |7 |8学习逻辑|9

| 70 | 5 |接受| 0.25695 | 0.16748 | 0.24349 | 0.28235 |资讯| NumNeighbors: 14 | | | | | | | | | |距离:cityblock | | 71 | |接受| 0.46276 | 0.7837 | 0.24349 | 0.28235 | |线性编码:onevsall | | | | | | | | | |λ:0.0033674 | | | | | | | | | |学习者:支持向量机|

|72 |4 |接受|0.28448 |6.0063 |0.24349 |0.27803 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 78 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 168 | | 73 | 4 | Accept | 0.6548 | 2.4452 | 0.24349 | 0.27803 | nb | DistributionNames: kernel | | | | | | | | | | Width: 2.6013 |

| 74 | 4 |接受| 0.42926 | 0.1085 | 0.24349 | 0.27803 | discr | Delta: 0.041145 | |0 |1 |2 |3 |4 |5 |6 |7 |8 Gamma: 0.34864 |9

|75 |2 |接受|0.24529 |222.53 |0.24349 |0.27803 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.25488 | | | | | | | | | | KernelScale: 0.0037823 | | 76 | 2 | Accept | 0.25217 | 21.217 | 0.24349 | 0.27803 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 19 | | 77 | 2 | Accept | 0.32516 | 0.19708 | 0.24349 | 0.27803 | tree | MinLeafSize: 5 |

|78 |6 |接受|0.43703 |0.87585 |0.24349 |0.27803 |线性|编码：onevsall | | | | | | | | | | Lambda: 0.013265 | | | | | | | | | | Learner: logistic |

|79 |3 |接受|0.25695 |2.1197 |0.24349 |0.27803 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 712.17 | | | | | | | | | | KernelScale: 79.244 | | 80 | 3 | Accept | 0.25456 | 2.276 | 0.24349 | 0.27803 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 22 | | | | | | | | | | LearnRate: 0.28501 | | | | | | | | | | MinLeafSize: 104 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 81 | 3 | Accept | 0.31529 | 3.001 | 0.24349 | 0.27803 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 22 | | | | | | | | | | LearnRate: 0.10996 | | | | | | | | | | MinLeafSize: 104 | | 82 | 3 | Accept | 0.42596 | 0.19157 | 0.24349 | 0.27803 | discr | Delta: 0.00034456 | | | | | | | | | | Gamma: 0.08223 |

| 83 | 5 |接受| 0.25456 | 7.1816 | 0.24349 | 0.27803 | |内核编码:onevsone | | | | | | | | | | KernelScale: 0.78697 | | | | | | | | | |λ:4.1197 e-06 | | 84 | |接受| 0.43015 | 0.092979 | 0.24349 | 0.27803 | discrδ:| 0.0069822 | | | | | | | | | |γ:0.49526 |

|85 |3 |接受|0.32905 |0.3807 |0.24349 |0.27803 |KNN |NumNeighbors：65 | | | | | | | | | | Distance: seuclidean | | 86 | 3 | Accept | 0.50194 | 0.58497 | 0.24349 | 0.27803 | linear | Coding: onevsall | | | | | | | | | | Lambda: 22.21 | | | | | | | | | | Learner: svm | | 87 | 3 | Accept | 0.30242 | 0.2907 | 0.24349 | 0.27803 | tree | MinLeafSize: 219 |

| 88 | 5 |接受| 0.28328 | 11.457 | 0.24349 | 0.27803 | |内核编码:onevsall | | | | | | | | | | KernelScale: 0.24613 | | | | | | | | | |λ:6.5582 e-06 | | 89 | |接受| 0.67903 | 3.2891 | 0.24349 | 0.27803 | |内核编码:onevsone | | | | | | | | | | KernelScale: 66.432 | | | | | | | | | |λ:0.00097982 |

| 90 | 3 |接受| 0.28059 | 0.15967 | 0.24349 | 0.27803 | nb | DistributionNames:正常| | | | | | | | | |宽度:南| | 91 | |接受| 0.74185 | 0.1332 | 0.24349 | 0.27803 | discrδ:| 89.479 | | | | | | | | | |γ:0.11568 | | 92 | |接受| 0.29704 | 1.6362 | 0.24349 | 0.27803 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.90434 | | | | | | | | | | KernelScale: 5.1972 |

| 93 | 4 |接受| 0.75232 | 6.2607 | 0.24349 | 0.27803 | |内核编码:onevsone | | | | | | | | | | KernelScale: 0.0025204 | | | | | | | | | |λ:0.0017056 | | 94 | |接受| 0.61203 | 2.7767 | 0.24349 | 0.27803 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 53 | | | | | | | | | | LearnRate:南| | | | | | | | | | MinLeafSize: 926 | | 95 | |接受| 0.42926 | 0.1032 | 0.24349 | 0.27803 | discrδ:| 0.00083172 | | | | | | | | | |γ:0.41309 |

|96 |2 |接受|0.24619 |3.3793 |0.24349 |0.2819 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 305.08 | | | | | | | | | | KernelScale: 1.4567 | | 97 | 2 | Accept | 0.71911 | 0.67791 | 0.24349 | 0.2819 | knn | NumNeighbors: 659 | | | | | | | | | | Distance: jaccard | | 98 | 2 | Accept | 0.37212 | 1.5638 | 0.24349 | 0.2819 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 15 | | | | | | | | | | LearnRate: 0.010706 | | | | | | | | | | MinLeafSize: 366 |

| 99 | 5 |接受| 0.28059 | 0.1674 | 0.24349 | 0.2819 | nb | DistributionNames:正常| | | | | | | | | |宽度:南| | 100 | |接受| 0.30093 | 0.10739 | 0.24349 | 0.2819 | |树MinLeafSize: 135 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 101 | |接受| 0.2767 | 5.3033 | 0.24349 | 0.2819 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 53 | | | | | | | | | | LearnRate: 0.012335 | | | | | | | | | | MinLeafSize: 5 | | 102 | |接受| 0.28597 | 2.062 | 0.24349 | 0.2819 |合奏|方法:RUSBoost | | | | | | | | | | NumLearningCycles: 23日| | | | | | | | | | LearnRate: 0.018926 | | | | | | | | | | MinLeafSize: 1 | | 103 | |接受| 0.61771 | 0.24182 | 0.24349 | 0.2819 | |树MinLeafSize: 1200 |

| 104 | 6 |接受| 0.25576 | 3.0306 | 0.24349 | 0.27907 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 38 | | | | | | | | | | LearnRate:南| | | | | | | | | | MinLeafSize: 28 |

|105 |3 |接受|0.31977 |1.4678 |0.24349 |0.27907 |线性|编码：onevsone | | | | | | | | | | Lambda: 0.040102 | | | | | | | | | | Learner: logistic | | 106 | 3 | Accept | 0.28059 | 0.15767 | 0.24349 | 0.27907 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 107 | 3 | Accept | 0.52976 | 3.7522 | 0.24349 | 0.27907 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 52 | | | | | | | | | | LearnRate: 0.0051271 | | | | | | | | | | MinLeafSize: 854 | | 108 | 3 | Accept | 0.3781 | 1.876 | 0.24349 | 0.27907 | linear | Coding: onevsall | | | | | | | | | | Lambda: 9.0139e-07 | | | | | | | | | | Learner: logistic |

| 109 | |最佳| 0.2429 | 2.1357 | 0.2429 | 0.27907 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.10541 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.061524 |9

|110 |3 |接受|0.30212 |15.324 |0.2429 |0.27907 |合奏|方法：袋| | | | | | | | | | NumLearningCycles: 249 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 292 | | 111 | 3 | Accept | 0.25905 | 6.4685 | 0.2429 | 0.27907 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 61 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 3 | | 112 | 3 | Accept | 0.4819 | 3.2352 | 0.2429 | 0.27907 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 239.8 | | | | | | | | | | KernelScale: 51.105 | | 113 | 3 | Accept | 0.72241 | 3.0692 | 0.2429 | 0.27907 | nb | DistributionNames: kernel | | | | | | | | | | Width: 7.4433 |

| 114 | 6 |接受| 0.45947 | 0.55863 | 0.27907 |线性|编码:onevsall | |0 |1 |2 |3 |5 |6 |7 |8 Lambda: 0.028513 |9 |1 |2 |3 |4 |5 |6 |8学习机|9

|115 |3 |接受|0.74185 |2.3519 |0.2429 |0.27907 |NB |DistributionNames：内核| | | | | | | | | | Width: 74.975 | | 116 | 3 | Accept | 0.29794 | 1.7602 | 0.2429 | 0.27907 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 14 | | | | | | | | | | LearnRate: 0.0011077 | | | | | | | | | | MinLeafSize: 2 | | 117 | 3 | Accept | 0.48549 | 14.081 | 0.2429 | 0.27907 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.36208 | | | | | | | | | | KernelScale: 0.061675 | | 118 | 3 | Accept | 0.38169 | 3.4566 | 0.2429 | 0.27907 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 6.0043 | | | | | | | | | | Lambda: 0.0033315 |

| 119 | 6 |接受| 0.24559 | 5.4474 | 0.2429 | 0.27907 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0059499 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.0045622 |9

最好| 120 | 4 | | 0.2408 | 1.6297 | 0.2408 | 0.27907 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.011671 | | | | | | | | | | KernelScale: 0.050076 | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 121 | |接受| 0.25725 | 15.492 | 0.2408 | 0.27907 |合奏|方法:AdaBoostM2 | | | | | | | | | | NumLearningCycles: 149 | | | | | | | | | | LearnRate: 0.10812 | | | | | | | | | | MinLeafSize: 1 | | 122 | |接受| 0.47831 | 0.088834 | 0.2408 | 0.27907 | |树MinLeafSize: 833 |

|123 |4 |最佳|0.2405 |1.4439 |0.2405 |0.24636 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.015035 | | | | | | | | | | KernelScale: 0.057715 |

| 124 | 4 |接受| 0.2417 | 1.5135 | 0.2405 | 0.24498 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0095934 |9 |0 |1 |2 |3 |4 |6 |7 |8 KernelScale: 0.058685 |9

| 125 | 4 b|最佳| 0.2399 | 1.4853 | 0.2399 | 0.24443 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.035844 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.08166 |9

| 126 | 4 |接受| 0.24529 | 63.489 | 0.2399 | 0.24408 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 405.36 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.3059 |9

| 127 | 4 |接受| 0.2417 | 1.4706 | 0.2399 | 0.24465 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.016665 |9 |0 |1 |2 |3 |4 |6 |7 |8 KernelScale: 0.083583 |9

| 128 | 4 |接受| 0.25546 | 1.4908 | 0.2399 | 0.24411 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.038736 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.55267 |9

| 129 | 4 |接受| 0.30841 | 1.885 | 0.2399 | 0.24408 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0015949 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.34759 |9

|130 |4 |接受|0.36494 |1.9172 |0.2399 |0.24384 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.017084 | | | | | | | | | | KernelScale: 1.9122 |

| 131 | 4 |接受| 0.30242 | 1.6419 | 0.2399 | 0.24389 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.033156 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 1.1823 |9

|132 |5 |接受|0.40203 |1.8764 |0.2399 |0.24422 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.022866 | | | | | | | | | | KernelScale: 2.5154 |

|133 |5 |接受|0.24349 |2.287 |0.2399 |0.24422 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.14375 | | | | | | | | | | KernelScale: 0.063056 |

|134 |5 |接受|0.2402 |1.598 |0.2399 |0.24365 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.012787 | | | | | | | | | | KernelScale: 0.061711 |

|135 |4 |接受|0.4843 |135.72 |0.2399 |0.24365 |SVM |编码：onevsall | | | | | | | | | | BoxConstraint: 0.0016902 | | | | | | | | | | KernelScale: 0.031528 | | 136 | 4 | Accept | 0.78193 | 10.536 | 0.2399 | 0.24365 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0024164 | | | | | | | | | | Lambda: 3.0566e-07 |

|编码:onevsall | |0 |1 |2 |3 |5 |6 |7 |8 BoxConstraint: 0.020039 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.05327 |9

| 138 | 4 |接受| 0.79061 | 10.427 | 0.2399 | 0.24207 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 0.0012217 |9 |0 |1 |2 |3 |5 |6 |8 Lambda: 1.8732e-06 |9

| 139 | 4 |接受| 0.24379 | 1.5859 | 0.2399 | 0.2419 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0026657 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.058261 |9

|140 |4 |接受|0.24379 |1.7337 |0.2399 |0.24721 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.02367 | | | | | | | | | | KernelScale: 0.037805 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 141 | |接受| 0.74604 | 9.898 | 0.2399 | 0.24721 | |内核编码:onevsone | | | | | | | | | | KernelScale: 0.03064 | | | | | | | | | |λ:4.6992 e-06 |

| 142 | 4 |接受| 0.24619 | 4.1003 | 0.2399 | 0.24822 |支持向量机|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.40789 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.047603 |9

|143 |4 |接受|0.24499 |9.2411 |0.2399 |0.24541 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.80115 | | | | | | | | | | KernelScale: 0.038152 |

| 144 | 4 |接受| 0.2405 | 1.389 | 0.2399 | 0.24287 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.018782 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.06909 |9

| 145 | 4 |接受| 0.26234 | 4.1253 | 0.2399 | 0.24287 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 3.0021 |9 |0 |1 |2 |3 |5 |6 |8 Lambda: 0.002498 |9

| 146 | 4 |接受| 0.24559 | 1.3591 | 0.2399 | 0.24345 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0015537 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.061601 |9

| 147 | 4 |接受| 0.39426 | 2.1758 | 0.2399 | 0.24345 |核心|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 KernelScale: 2.7611 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.058785 |9

| 148 | 4 |接受| 0.44032 | 9.2619 | 0.2399 | 0.24345 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 0.10145 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.247e-06 |9

|149 |4 |接受|0.26114 |5.2181 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 12.896 | | | | | | | | | | Lambda: 5.2477e-07 |

| 150 | 4 |接受| 0.50374 | 10.004 | 0.2399 | 0.24345 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 0.073856 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.1532e-06 |9

|151 |4 |接受|0.27939 |5.1821 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 16.882 | | | | | | | | | | Lambda: 3.1205e-07 |

|152 |4 |接受|0.27819 |4.5003 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 18.796 | | | | | | | | | | Lambda: 1.2989e-05 |

| 153 | 4 |接受| 0.27819 | 4.7288 | 0.2399 | 0.24345 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 20.531 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 7.0733e-07 |9

|154 |4 |接受|0.64792 |3.5128 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 13.339 | | | | | | | | | | Lambda: 0.015595 |

| 155 | 4 |接受| 0.36135 | 4.5167 | 0.2399 | 0.24345 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 23.171 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 8.9366e-07 |9

|156 |4 |接受|0.31947 |4.9979 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 23.387 | | | | | | | | | | Lambda: 3.5105e-07 |

|157 |4 |接受|0.25935 |4.7775 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 9.5518 | | | | | | | | | | Lambda: 4.9153e-06 |

| 158 | 4 |接受| 0.30093 | 3.849 | 0.2399 | 0.24345 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 7.3599 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0025979 |9

|159 |4 |接受|0.35028 |4.1896 |0.2399 |0.24345 |内核|编码：onevsone | | | | | | | | | | KernelScale: 23.149 | | | | | | | | | | Lambda: 8.8313e-06 |

|160 |4 |接受|0.24559 |1.3885 |0.2399 |0.24519 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.0027832 | | | | | | | | | | KernelScale: 0.069321 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 161 | 4 | Accept | 0.26324 | 4.97 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 11.576 | | | | | | | | | | Lambda: 5.1587e-06 |

| 162 | 3 |接受| 0.24379 | 430.57 | 0.2399 | 0.24519 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 314.58 | | | | | | | | | | KernelScale: 0.055576 | | 163 | |接受| 0.26742 | 4.2066 | 0.2399 | 0.24519 | |内核编码:onevsone | | | | | | | | | | KernelScale: 9.9599 | | | | | | | | | |λ:0.0002376 |

|164 |6 |接受|0.25546 |7.3897 |0.2399 |0.24519 |内核|编码：onevsone | | | | | | | | | | KernelScale: 0.66256 | | | | | | | | | | Lambda: 4.9136e-06 |

|165 |3 |接受|0.29165 |4.0149 |0.2399 |0.24519 |内核|编码：onevsone | | | | | | | | | | KernelScale: 8.7912 | | | | | | | | | | Lambda: 0.00065247 | | 166 | 3 | Accept | 0.28059 | 0.16557 | 0.2399 | 0.24519 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 167 | 3 | Accept | 0.242 | 1.5393 | 0.2399 | 0.24519 | linear | Coding: onevsone | | | | | | | | | | Lambda: 4.114e-07 | | | | | | | | | | Learner: svm | | 168 | 3 | Accept | 0.66437 | 1.3826 | 0.2399 | 0.24519 | linear | Coding: onevsone | | | | | | | | | | Lambda: 1.5345 | | | | | | | | | | Learner: logistic |

| 169 | 6 |接受| 0.30302 | 3.6462 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 10.456 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0012729 |9

| 170 | 3 |接受| 0.36584 | 9.8828 | 0.2399 | 0.24519 | |内核编码:onevsone | | | | | | | | | | KernelScale: 0.12329 | | | | | | | | | |λ:5.7761 e-06 | | 171 | |接受| 0.2767 | 2.2185 | 0.2399 | 0.24519 | nb | DistributionNames:内核| | | | | | | | | |宽度:0.013013 | | 172 | |接受| 0.29016 | 10.253 | 0.2399 | 0.24519 |合奏|方法:袋| | | | | | | | | | NumLearningCycles: 161 | | | | | | | | | | LearnRate:南| | | | | | | | | | MinLeafSize:233 | | 173 | 3 |接受| 0.30212 | 0.17493 | 0.2399 | 0.24519 |树| MinLeafSize: 125 |0

| 174 | 6 |接受| 0.36853 | 3.9359 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 14.54 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0018156 |9

|175 |3 |接受|0.2399 |1.5716 |0.2399 |0.24519 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.018425 | | | | | | | | | | KernelScale: 0.05816 | | 176 | 3 | Accept | 0.25426 | 2.1785 | 0.2399 | 0.24519 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 21 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 10 | | 177 | 3 | Accept | 0.44421 | 0.69127 | 0.2399 | 0.24519 | linear | Coding: onevsall | | | | | | | | | | Lambda: 9.8986e-08 | | | | | | | | | | Learner: svm | | 178 | 3 | Accept | 0.24349 | 4.7623 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 7.5839 | | | | | | | | | | Lambda: 6.1706e-06 |

| 179 | 6 |接受| 0.32546 | 3.8654 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 11.818 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0021157 |9

|180 |3 |接受|0.25396 |4.8254 |0.2399 |0.24519 |内核|编码：onevsall | | | | | | | | | | KernelScale: 3.2686 | | | | | | | | | | Lambda: 1.4612e-06 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 181 | 3 | Accept | 0.51391 | 3.1699 | 0.2399 | 0.24519 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.11591 | | | | | | | | | | KernelScale: 495.17 | | 182 | 3 | Accept | 0.2417 | 1.6756 | 0.2399 | 0.24519 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 7.202 | | | | | | | | | | KernelScale: 1.5776 | | 183 | 3 | Accept | 0.28059 | 0.59112 | 0.2399 | 0.24519 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |

|184 |6 |接受|0.31259 |3.7378 |0.2399 |0.24519 |内核|编码：onevsone | | | | | | | | | | KernelScale: 12.684 | | | | | | | | | | Lambda: 0.0015075 |

| 185 | 4 |接受| 0.25277 | 12.458 | 0.2399 | 0.24519 | |内核编码:onevsall | | | | | | | | | | KernelScale: 0.66323 | | | | | | | | | |λ:3.1391 e-06 | | 186 | |接受| 0.74185 | 4.433 | 0.2399 | 0.24519 | |内核编码:onevsone | | | | | | | | | | KernelScale: 513.67 | | | | | | | | | |λ:0.013813 | | 187 | |接受| 0.29165 | 9.8216 | 0.2399 | 0.24519 | |内核编码:onevsall | | | | | | | | | | KernelScale: 0.1806 | | | | | | | | | |λ:5.7403 e-05 |

| 188 | 4 |接受| 0.24589 | 5.2677 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 4.5409 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.6179e-06 |9

| 189 | 4 |接受| 0.25875 | 4.9459 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 KernelScale: 13.189 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.0438e-06 |9

| 190 | 4 |接受| 0.29794 | 9.4099 | 0.2399 | 0.24519 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 0.24389 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.5305e-06 |9

| 191 | 4 |接受| 0.2399 | 1.4613 | 0.2399 | 0.24482 |支持向量机|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 BoxConstraint: 0.017676 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.05684 |9

| 192 | 4 |接受| 0.6198 | 2.1987 | 0.2399 | 0.24188 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.037629 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 4.6813 |9

| 193 | 4 |接受| 0.24589 | 3.6814 | 0.2399 | 0.24395 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.48718 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.071987 |9

|194 |4 |接受|0.30362 |1.6935 |0.2399 |0.24179 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.036257 | | | | | | | | | | KernelScale: 1.496 |

| 195 | 4 |接受| 0.24828 | 5.6169 | 0.2399 | 0.24179 |核心|编码:onevsall | |0 |1 |1 |5 |6 |7 |8 KernelScale: 2.0998 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.7852e-06 |9

|196 |4 |接受|0.2405 |1.4008 |0.2399 |0.2416 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.025005 | | | | | | | | | | KernelScale: 0.079623 |

|197 |4 |接受|0.25695 |10.376 |0.2399 |0.2416 |内核|编码：onevsall | | | | | | | | | | KernelScale: 0.54744 | | | | | | | | | | Lambda: 2.181e-06 |

| 198 | 4 |接受| 0.2429 | 1.8889 | 0.2399 | 0.25253 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.090859 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.066231 |9

|199 |4 |接受|0.24858 |4.1934 |0.2399 |0.25163 |内核|编码：onevsone | | | | | | | | | | KernelScale: 2.5004 | | | | | | | | | | Lambda: 0.0016318 |

|200 |4 |接受|0.2408 |1.4334 |0.2399 |0.24216 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.029253 | | | | | | | | | | KernelScale: 0.063477 |

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:价值|||工人|结果||运行|（观察到的）| (estim.) | | | |==============================================================================================================================| | 201 | 4 | Accept | 0.25067 | 1.4766 | 0.2399 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.01517 | | | | | | | | | | KernelScale: 0.27169 |

|202 |4 |接受|0.29794 |1.8479 |0.2399 |0.24143 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.20149 | | | | | | | | | | KernelScale: 2.5463 |

| 203 | 4 |接受| 0.24589 | 6.5027 | 0.2399 | 0.24148 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.30831 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.028787 |9

|204 |4 |接受|0.24678 |4.2346 |0.2399 |0.24148 |内核|编码：onevsone | | | | | | | | | | KernelScale: 1.5999 | | | | | | | | | | Lambda: 0.0022599 |

|205 |5 |接受|0.28148 |4.267 |0.2399 |0.24148 |内核|编码：onevsone | | | | | | | | | | KernelScale: 3.1926 | | | | | | | | | | Lambda: 0.0039213 |

|206 |5 |接受|0.25366 |4.6995 |0.2399 |0.24148 |内核|编码：onevsone | | | | | | | | | | KernelScale: 2.2861 | | | | | | | | | | Lambda: 0.0023336 |

| 207 | 6 |接受| 0.28986 | 4.6245 | 0.2399 | 0.24148 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 2.8603 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.0072539 |9

|208 |6 |接受|0.25067 |4.9866 |0.2399 |0.24148 |内核|编码：onevsone | | | | | | | | | | KernelScale: 1.0901 | | | | | | | | | | Lambda: 0.0099896 |

|209 |6 |接受|0.26802 |4.837 |0.2399 |0.24148 |内核|编码：onevsone | | | | | | | | | | KernelScale: 1.6265 | | | | | | | | | | Lambda: 0.01081 |

|210 |6 |接受|0.24499 |56.624 |0.2399 |0.24154 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 8.7909 | | | | | | | | | | KernelScale: 0.05728 |

| 211 | 6 |接受| 0.25097 | 5.1646 | 0.2399 | 0.24154 |核心|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 KernelScale: 0.94502 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.012321 |9

| 212 | 6 |接受| 0.27879 | 4.737 | 0.2399 | 0.24154 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 1.5962 |9 |0 |1 |2 |3 |4 |6 |8, Lambda: 0.014927 |9

|213 |6 |接受|0.28537 |4.6966 |0.2399 |0.24154 |内核|编码：onevsone | | | | | | | | | | KernelScale: 1.858 | | | | | | | | | | Lambda: 0.012473 |

|214 |5 |接受|0.24529 |172.16 |0.2399 |0.24154 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 32.749 | | | | | | | | | | KernelScale: 0.061248 | | 215 | 5 | Accept | 0.25127 | 4.7942 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.91593 | | | | | | | | | | Lambda: 0.013134 |

|216 |5 |接受|0.277 |4.1458 |0.2399 |0.24154 |内核|编码：onevsone | | | | | | | | | | KernelScale: 1.4949 | | | | | | | | | | Lambda: 0.015008 |

| 217 | 5 |接受| 0.30123 | 3.9237 | 0.2399 | 0.24154 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 1.2069 |9 |0 |1 |2 |3 |4 |6 |8 Lambda: 0.071401 |9

|218 |5 |接受|0.30691 |2.555 |0.2399 |0.24154 |内核|编码：onevsall | | | | | | | | | | KernelScale: 1.0328 | | | | | | | | | | Lambda: 0.013653 |

| 219 | 5 |接受| 0.29106 | 4.1175 | 0.2399 | 0.24154 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 1.9718 |9 |0 |1 |2 |3 |5 |6 |8 Lambda: 0.014791 |9

| 220 | 5 |接受| 0.27341 | 4.054 | 0.2399 | 0.24154 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 1.0948 |9 |0 |1 |2 |3 |5 |6 |8 Lambda: 0.029433 |9

| = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | | Iter |活跃Eval客观客观| | | BestSoFar | BestSoFar |学生| Hyperparameter:值| | |工人结果| | |运行时|(观察)| (estim) | | | | = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | | 221 | |接受| 0.24499 | 1.7385 | 0.2399 | 0.24325 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 0.019711 | | | | | | | | | | KernelScale: 0.20418 |

| 222 | 5 |接受| 0.32576 | 3.5611 | 0.2399 | 0.24325 |核心|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 KernelScale: 0.96406 |9 |0 |1 |2 |3 |4 |6 |8 Lambda: 0.19447 |9

| 223 | 5 |接受| 0.33084 | 4.3505 | 0.2399 | 0.24325 |核心|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 KernelScale: 23.853 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 4.1268e-07 |9

| 224 | 5 |接受| 0.25187 | 1.6516 | 0.2399 | 0.24139 |支持向量机|编码:onevsone | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.0019286 |9 |0 |1 |2 |3 |4 |5 |6 |8 KernelScale: 0.10433 |9

|225 |5 |接受|0.25037 |4.822 |0.2399 |0.24139 |内核|编码：onevsone | | | | | | | | | | KernelScale: 0.71239 | | | | | | | | | | Lambda: 0.015711 |

| 226 | 5 |接受| 0.3452 | 10.241 | 0.2399 | 0.24139 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 0.14973 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 1.4037e-06 |9

| 227 | 5 |接受| 0.26204 | 9.4337 | 0.2399 | 0.24139 |核心|编码:onevsone | |0 |1 |1 |5 |6 |7 |8 KernelScale: 0.41479 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 3.5263e-06 |9

|228 |5 |接受|0.25097 |1.604 |0.2399 |0.24158 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.018254 | | | | | | | | | | KernelScale: 0.2875 |

| 229 | 5 |接受| 0.36075 | 4.6692 | 0.2399 | 0.24158 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 24.105 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 3.803e-07 |9

| 230 | 4 |接受| 0.24559 | 465.69 | 0.2399 | 0.24158 |支持向量机|编码:onevsone | | | | | | | | | | BoxConstraint: 318.11 | | | | | | | | | | KernelScale: 0.061933 | | 231 | |接受| 0.29016 | 4.7925 | 0.2399 | 0.24158 | |内核编码:onevsone | | | | | | | | | | KernelScale: 1.3656 | | | | | | | | | |λ:0.035821 |

|232 |4 |接受|0.24649 |1.5588 |0.2399 |0.24083 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.0013393 | | | | | | | | | | KernelScale: 0.068648 |

|233 |4 |接受|0.25426 |14221 |0.2399 |0.24157 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.080107 | | | | | | | | | | KernelScale: 0.72702 |

|234 |4 |接受|0.31558 |1.8074 |0.2399 |0.24166 |SVM |编码：onevsone | | | | | | | | | | BoxConstraint: 0.067209 | | | | | | | | | | KernelScale: 2.6645 |

| 235 | 4 |接受| 0.3443 | 3.9888 | 0.2399 | 0.24166 |核心|编码:onevsall | |0 |1 |1 |5 |6 |7 |8 KernelScale: 24.211 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 5.9888e-07 |9

|编码:onevsone | |0 |1 |2 |3 |5 |6 |7 |8 BoxConstraint: 0.64044 |9 |0 |1 |2 |3 |4 |6 |7 |8 KernelScale: 0.072822 |9

| 237 | 4 |接受| 0.24948 | 4.0124 | 0.2399 | 0.24171 |核心|编码:onevsone | |0 |1 |1 |3 |5 |6 |7 |8 KernelScale: 0.72883 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 0.020466 |9

| 238 | 4 |接受| 0.44601 | 3.6883 | 0.2399 | 0.2408 |支持向量机|编码:onevsall | |0 |1 |3 |5 |6 |7 |8 BoxConstraint: 0.016081 |9 |0 |1 |2 |3 |4 |6 |6 |8 KernelScale: 0.065807 |9

| 239 | 4 |接受| 0.25636 | 4.1374 | 0.2399 | 0.2408 |核心|编码:onevsall | |0 |1 |1 |5 |6 |7 |8 KernelScale: 4.6533 |9 |0 |1 |2 |3 |4 |6 |7 |8 Lambda: 9.4081e-07 |9

|240 |4 |接受|0.26114 |5.0408 |0.2399 |0.2408 |内核|编码：onevsone | | | | | | | | | | KernelScale: 12.554 | | | | | | | | | | Lambda: 1.5929e-06 |

__________________________________________________________优化完成。240 MaxObjectiveEvaluations达到。总功能评价：240总经过时间：1954.7195秒。总目标函数评估时间：2414.1608最佳观察到的可行点是一个多分类SVM模型：编码（ECOC）：onevsone BoxConstraint：0.035844 KernelScale：0.08166观察目标函数值= 0.2399估计目标函数值= 0.24266功能评估时间= 1.4853最佳估计可行（根据型号）点是一个多分类SVM模型：编码（ECOC）：onevsone BoxConstraint：0.018425 KernelScale：0.05816估计目标函数值= 0.2408估计函数评估时间= 1.6217

评估测试集的性能

该模型MDL的最优点对应于贝叶斯优化中的最优点“min-visited-mean”标准。为了衡量如何该模型将新数据在模型的观测交叉验证的精度进行，外观（cvAccuracy)及其基于贝叶斯优化(estimatedAccuracy）。

[x, ~,迭代]= bestPoint(结果,“标准”,“min-visited-mean”);cvError = Results.ObjectiveTrace（迭代）;cvAccuracy = 1  -  cvError

cvAccuracy = 0.7601

estimatedError = predictObjective(结果,x);estimatedAccuracy = 1 - estimatedError

estimatedAccuracy = 0.7592

评估模型在测试集上的性能。根据结果创建一个混淆矩阵，并在混淆矩阵中指定类的顺序。

testAccuracy = 1  - 损失（MDL，XTEST，YTest）

testAccuracy = 0.7438

厘米= confusionchart（YTest，预测（MDL，XTEST））;sortClasses（厘米，{“AAA”,“AA”,'一个',“BBB”,'BB','B',“CCC”})

输入参数

全部收缩

`资源描述`- - - - - -样本数据
表

示例数据，指定为一个表。每行资源描述对应一个观测值，每一列对应一个预测值。可选地,资源描述可以包含响应变量的一个附加列。不接受多列变量和单元数组(字符向量的单元数组除外)。

如果资源描述包含响应变量，并且您希望在其中使用所有剩余的变量资源描述作为预测，然后使用指定响应变量ResponseVarName。

如果资源描述包含响应变量，并且只希望使用其余变量的子集资源描述作为预测器，指定使用的公式公式。

如果资源描述不包含响应变量，使用指定响应变量Y。响应变量的长度和行数资源描述必须是相等的。

注意

fitcauto不支持线性或内核分万博1manbetx类模型的表。如果你愿意的话学习者包括“线性”或'核心'模型，你无法提供资源描述,ResponseVarName,或公式。供给预测数据的矩阵（X)及一系列回应(Y)。

数据类型：表

`ResponseVarName`- - - - - -响应变量名
变量的命名`资源描述`

响应变量名，指定为变量的名称资源描述。

您必须指定ResponseVarName为一个字符向量或标量的字符串。例如，如果响应变量Y存储为Tbl.Y，然后将其指定为'Y'。否则，软件将处理的所有列资源描述,包括Y，作为预测训练模型时。

响应变量必须是明确的，字符或字符串数组，逻辑或数字载体，或字符向量的单元阵列。如果Y为字符数组，则响应变量的每个元素必须对应于数组的一行。

一个好的实践是使用一会名称 - 值对的参数。

数据类型：字符|串

`公式`- - - - - -响应变量和预测变量子集的解释模型
特征向量|字符串标量

响应变量和预测变量子集的解释模型，在表单中指定为字符向量或字符串标量“Y ~ X1 + X2 + X3”。在这种形式下，Y表示响应变量，和X1,X2和X3代表预测变量。

要指定变量的一个子集资源描述作为训练模型的预测器，使用一个公式。如果你指定了一个公式，那么软件不使用任何变量资源描述没有出现在公式。

公式中的变量名必须是其中的两个变量名资源描述(Tbl.Properties.VariableNames)和有效的MATLAB^®身份标识。

您可以验证的变量名中资源描述通过使用isvarname函数。下面的代码返回logical1(真正的）对于具有合法的变量名每个变量。

cellfun（@ isvarname，Tbl.Properties.VariableNames）

如果变量名资源描述是无效的，然后使用它们转换matlab.lang.makeValidName函数。

Tbl.Properties.VariableNames= matlab.lang.makeValidName(Tbl.Properties.VariableNames);

数据类型：字符|串

`Y`- - - - - -类标签
数字矢量|分类矢量|逻辑向量|字符数组|字符串数组|字符向量的单元阵列

类标签，指定为数字，类别，或逻辑向量，一个字符或字符串数组，或字符向量的单元阵列。

如果Y是一个字符数组，那么类标签的每个元素必须对应于数组的一行。
长度Y以及行数资源描述或X必须是相等的。
一个好的实践是使用一会名称 - 值对的参数。

数据类型：单|双|分类|合乎逻辑|字符|串|细胞

`X`- - - - - -预测数据
数字矩阵

预测数据，指定为数值矩阵。

每行X对应一个观测值，每一列对应一个预测值。

长度Y以及行数X必须是相等的。

按照谓词出现的顺序指定它们的名称X，使用PredictorNames名称 - 值对的参数。

数据类型：单|双

注意

该软件把为NaN，空字符向量（“”），空字符串（”“）<失踪>和<定义>元素作为缺失数据。该软件去除对应于在响应变量缺失值的数据行。然而，缺失值的预测数据处理X或资源描述不同的模型(或学习者)。

名称-值对的观点

指定可选的用逗号分隔的对名称，值参数。的名字是参数的名称和价值是对应的值。的名字必须出现引号内。您可以按照任何顺序指定多个名称和值对参数Name1, Value1,…,的家。

例子:“HyperparameterOptimizationOptions”、结构(“MaxObjectiveEvaluations”, 200年,“详细”,2)指定运行优化过程的200次迭代(即，尝试200个模型超参数组合)，并在命令窗口中显示关于下一个要评估的模型超参数组合的信息。

优化选项

全部收缩

`“学习者”`- - - - - -分类模型的类型
`'汽车'`(默认)|`'所有'`|`“所有线性”`|`“all-nonlinear”`|一个或多个学习者的名字

类型分类模型在优化过程中尝试，指定为逗号分隔对组成“学习者”并且在第二表下方在所述第一表中的值或一个或多个学习者名称。指定多个学习者名作为字符串或单元阵列。

价值	描述
`'汽车'`	`fitcauto`自动选择学习者的子集，适合于特定的预测和响应数据。学习者可以从默认的不同型号超参数值。欲了解更多信息，请参阅自动选择学习者。
`'所有'`	`fitcauto`选择所有可能的学习者。
`“所有线性”`	`fitcauto`选择所有线性学习者：`“discr”`(线性判别型)`“线性”`和`“支持向量机”`(带有线性核)
`“all-nonlinear”`	`fitcauto`选择所有非线性学习者：`“discr”`(对于二次判别型)`'合奏'`,`'核心'`,`'KNN'`,`“注”`,`“支持向量机”`（具有高斯或多项式内核），和`'树'`。

学习者名	描述
`“discr”`	判别分析分类器
`'合奏'`	系综分类模型
`'核心'`	内核的分类模型
`'KNN'`	k近邻模型
`“线性”`	线性分类模型
`“注”`	朴素贝叶斯分类器
`“支持向量机”`	万博1manbetx支持向量机分类器
`'树'`	二元决策分类树

例子:“学习者”,“所有”

例子:“学习者”，“合奏”

例子:'学习者'，{ '支持向量机'， '树'}

数据类型：字符|串|细胞

`'OptimizeHyperparameters'`- - - - - -Hyperparameters优化
`'汽车'`(默认)|`'所有'`

要优化的超参数，指定为逗号分隔对组成的'OptimizeHyperparameters'和'汽车'或'所有'。可优化的超参数取决于模型(或学习器)，如下表所述。

学习者名	Hyperparameters为`'汽车'`	额外Hyperparameters`'所有'`	笔记
`“discr”`	`三角洲`,`γ`	`DiscrimType`	当`学习者`值`“所有线性”`中，`fitcauto`函数在`DiscrimType`的值`“线性”`,`“diaglinear”`和`“伪线性”`，不管`OptimizeHyperparameters`价值。当`学习者`值`“all-nonlinear”`中，`fitcauto`函数在`DiscrimType`的值`“二次”`,`“diagquadratic”`和`“pseudoquadratic”`，不管`OptimizeHyperparameters`价值。欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`。
`'合奏'`	`方法`,`NumLearningCycles`,`LearnRate`,`MinLeafSize`	`MaxNumSplits`,`NumVariablesToSample`,`SplitCriterion`	当合奏`方法`值是升压的方法，该合奏`NumBins`值`50`。欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`。
`'核心'`	`KernelScale`,`LAMBDA`,`编码`（仅适用于三个或更多个类）	`学习者`,`NumExpansionDimensions`	欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`和`OptimizeHyperparameters`（对于三个或更多个类只）。
`'KNN'`	`距离`,`NumNeighbors`	`DistanceWeight`,`指数`,`标准化`	欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`。
`“线性”`	`LAMBDA`,`学习者`,`编码`（仅适用于三个或更多个类）	`正则化`	欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`和`OptimizeHyperparameters`（对于三个或更多个类只）。
`“注”`	`DistributionNames`,`宽度`	`核心`	欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`。
`“支持向量机”`	`BoxConstraint`,`KernelScale`,`编码`（仅适用于三个或更多个类）	`KernelFunction`,`PolynomialOrder`,`标准化`	当`学习者`值`“所有线性”`中，`fitcauto`功能不优化`KernelFunction`或`PolynomialOrder`hyperparameters当`OptimizeHyperparameters`值`'所有'`。当`学习者`值`“all-nonlinear”`中，`fitcauto`函数在`KernelFunction`的值`“高斯”`和`“多项式”`，不管`OptimizeHyperparameters`价值。欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`和`OptimizeHyperparameters`（对于三个或更多个类只）。
`'树'`	`MinLeafSize`	`MaxNumSplits`,`NumVariablesToSample`,`SplitCriterion`	欲了解更多信息，包括超参数搜索范围，请参阅`OptimizeHyperparameters`。

注意

什么时候学习者是否设置为其他值'汽车'，对于模型超参数的默认值不被优化匹配默认拟合函数值，除非在表中注释中另有说明。什么时候学习者被设置为'汽车'，根据训练数据的特征，优化的超参数搜索范围和未优化的超参数值都可以变化。

例子:'OptimizeHyperparameters'， '所有'

`“HyperparameterOptimizationOptions”`- - - - - -选择优化
结构

为优化选项，指定为逗号分隔的一对组成的“HyperparameterOptimizationOptions”和结构。在结构上所有字段都是可选的。

字段名	值	默认
`MaxObjectiveEvaluations`	目标函数评估的最大数量	`30 * L`,在那里`l`是学习者数
`MaxTime`	时限，指定为正实数。时间限制以秒为单位，用`抽搐`和`toc`。运行时间可超过`MaxTime`因为`MaxTime`不中断功能的评价。	`正`
`ShowPlots`	指示是否显示绘图的逻辑值。如果`真正的`这田间小区的最佳观察到的和估计的的目标函数值（迄今为止）针对迭代次数。	`真正的`
`SaveIntermediateResults`	逻辑值，指示是否保存结果。如果`真正的`，该字段将覆盖名为工作空间可变`“BayesoptResults”`在每次迭代。该变量是一个`BayesianOptimization`宾语。	`假`
`详细的`	显示在命令行： `0`- 不重复显示 `1`——迭代显示 `2`- 有关的下一个点的附加信息的迭代显示待评估	`1`
`UseParallel`	逻辑值指示是否并行运行贝叶斯优化，这需要并行计算工具箱™。由于并行定时的nonreproducibility，平行贝叶斯优化不一定得到重复的结果。	`假`
`重新分割`	逻辑值指示是否在每次迭代重新对交叉验证。如果`假`，优化器使用单个分区进行优化。 `真正的`通常给出最健壮的结果，因为该设置考虑了分区噪声。然而，为了好的结果，`真正的`至少需要两倍多的功能评价。	`假`
只有指定的以下三个选项之一。
`CVPartition`	`cvpartition`创建的对象,`cvpartition`	`'Kfold'，5`如果你不指定任何交叉验证领域
`坚持`	标量范围`(0,1)`代表抵抗分数
`Kfold`	大于1的整数

例子:“HyperparameterOptimizationOptions”、结构(UseParallel,真的)

数据类型：结构

分类选项

全部收缩

`'CategoricalPredictors'`- - - - - -分类预测列表
正整数的向量|逻辑向量|汉字字模|字符串数组|字符向量的单元阵列|`'所有'`

分类预测列表中，指定为逗号分隔的一对组成的'CategoricalPredictors'这个表中的一个值。

价值	描述
正整数的向量	向量中的每个条目是对应于预测数据的列（索引值`X`或`资源描述`），其中包含一个分类变量。
逻辑向量	一个`真正的`条目表示预测数据的对应的列（`X`或`资源描述`）为分类变量。
汉字字模	矩阵的每一行都是一个预测变量的名称。名称必须与其中的项相匹配`PredictorNames`。垫多余的空格的名字，这样的字符矩阵的每一行具有相同的长度。
串阵列或字符向量的单元阵列	数组中的每个元素都是一个预测变量的名称。名称必须与其中的项相匹配`PredictorNames`。
`'所有'`	所有的预测都是绝对的。

默认情况下，如果预测器数据在表中(资源描述）fitcauto假定一个变量是分类，如果它是一个逻辑向量，分类矢量，字符数组，字符串数组，或字符向量的单元阵列。然而，学习者在使用决策树假设数学有序分类向量是连续变量。如果预测数据是矩阵（X）fitcauto假设所有的预测都是连续的。要确定任何其他预测作为分类预测，指定它们通过使用'CategoricalPredictors'名称 - 值对的参数。

有关拟合函数如何处理分类谓词的更多信息，请参见虚拟变量自动生成。

例子:“CategoricalPredictors”、“所有”

数据类型：单|双|合乎逻辑|字符|串|细胞

`“类名”`- - - - - -用于培训的类的名称
分类数组|字符数组|字符串数组|逻辑向量|数字矢量|字符向量的单元阵列

类名称，以用于训练，指定为逗号分隔的一对组成的“类名”和类别、字符或字符串数组、逻辑或数字向量或字符向量的单元格数组。一会必须具有相同的数据类型Y。

如果一会是字符数组，则每个元素必须对应数组的一行。

采用“类名”至：

培训期间订购的类。
指定与类顺序对应的任何输入或输出参数维度的顺序。例如,使用“类名”来指定尺寸的顺序成本或按列顺序分类得分返回预测。
选择要进行培训的类的子集。例如，假设所有不同的类名的集合Y是{ 'A'， 'B'， 'C'}。使用来自类的观察来训练模型“一个”和“c”只是，指定“类名”,{' a ', ' c '}。

作为默认值一会是集合所有不同的类名Y。

例子:“类名”,{' b ', ' g '}

数据类型：分类|字符|串|合乎逻辑|单|双|细胞

`“成本”`- - - - - -误分类代价
方阵|结构数组

误分类成本，指定为逗号分隔的一对组成的“成本”和一个方阵或结构阵列。

如果你指定一个方阵成本观察的真正类别是一世，然后成本（I，J）将一个点分类的成本是多少Ĵ。也就是说，行对应于真正的类，列对应于预测的类。的相应行和列的类顺序成本，并指定一会名称 - 值对的参数。
如果你指定了一个结构年代，那么它必须有两个字段：
- S.ClassNames，其中包含类名作为与之具有相同数据类型的变量Y
- S.ClassificationCosts，其中包含按in排序的行和列的成本矩阵S.ClassNames

作为默认值成本是那些（K） - 眼（K）,在那里K是不同类的数目。

例子:“成本”,[0 1;2 0]

数据类型：单|双|结构

`'PredictorNames'`- - - - - -预测变量名
唯一名称的字符串数组|唯一的字符向量的单元阵列

预测器变量名，指定为逗号分隔的一对组成的'PredictorNames'和唯一名称的字符串数组或唯一字符向量的单元格数组。的功能'PredictorNames'取决于所提供的训练数据的方式。

如果提供X和Y，然后你可以使用'PredictorNames'给预测变量X名。
- 名字的顺序PredictorNames必须对应的列顺序X。也就是说,PredictorNames {1}是X（：，1）,PredictorNames {2}是X（：，2）等等。同时,大小(X, 2)和numel（PredictorNames）必须是相等的。
- 默认，PredictorNames是{x1, x2,…}。
如果提供资源描述，然后你可以使用'PredictorNames'选择在训练中使用的预测变量。也就是说,fitcauto只使用预测变量inPredictorNames和在训练响应变量。
- PredictorNames必须是一个子集Tbl.Properties.VariableNames并且不能包含响应变量的名称。
- 默认，PredictorNames包含所有预测变量的名称。
- 一个好的实践是指定使用其中之一进行培训的预测器'PredictorNames'或公式只有。

例子:'PredictorNames'，{ 'SepalLength'， 'SepalWidth'， 'PetalLength'， 'PetalWidth'}

数据类型：串|细胞

`“之前”`- - - - - -先验概率
`'经验'`(默认)|`“统一”`|数字矢量|结构数组

每个类的先验概率，指定为逗号分隔的对“之前”并在此表中的值。

价值	描述
`'经验'`	类先验概率是其中的类相对频率`Y`。
`“统一”`	所有类先验概率等于1 /K,在那里K是的类的数量。
数字矢量	每个元素是一个类的先验概率。根据命令元素`MDL.ClassNames`或使用指定的顺序`一会`名称 - 值对的参数。该软件的标准化元素总和`1`。
结构	一个结构`年代`两个字段: `S.ClassNames`将类名作为与类型相同的变量包含`Y`。 `S.ClassProbs`包含相应先验概率的向量。该软件的标准化元素总和`1`。

例子:“之前”,结构(“类名”,{{' b ', ' g '}}, ClassProbs, 1:2)

数据类型：单|双|字符|串|结构

`“ResponseName”`- - - - - -响应变量名
`'Y'`(默认)|特征向量|字符串标量

响应变量名，指定为逗号分隔的对“ResponseName”和字符向量或标量的字符串。

如果提供Y，然后你可以使用“ResponseName”指定响应变量的名称。
如果提供ResponseVarName或公式，则不能使用“ResponseName”。

例子:'ResponseName'， '响应'

数据类型：字符|串

`'ScoreTransform'`- - - - - -分数转换
`'没有'`(默认)|`“doublelogit”`|`“invlogit”`|`“ismax”`|`'Logit模型'`|功能手柄|……

得分变换，指定为逗号分隔的一对组成的'ScoreTransform'和字符矢量，标量的字符串，或功能句柄。

此表总结了可用的特征向量和字符串标量。

价值	描述
`“doublelogit”`	1 / (1 +Ë^-2X)
`“invlogit”`	日志（X/ (1 -X））
`“ismax”`	将得分最大的类的得分设置为1，并将所有其他类的得分设置为0
`'Logit模型'`	1 / (1 +Ë^{- - - - - -X})
`'没有'`或`“身份”`	X（无转换）
`“标志”`	-1X<0 0X= 0 1X> 0
`“对称”`	2X- 1
`“symmetricismax”`	将得分最大的类的得分设置为1，并将所有其他类的得分设置为-1
`“symmetriclogit”`	2 /（1 +Ë^{- - - - - -X})- 1

对于MATLAB功能，或者你定义一个函数，使用它的功能句柄得分变换。功能句柄必须接受矩阵（原始分）并返回相同的尺寸（转化分数）的矩阵。

例子:“ScoreTransform”、“分对数的

数据类型：字符|串|function_handle

`“权重”`- - - - - -观察权重
积极的数字矢量|变量的命名`资源描述`

观察权重，指定为逗号分隔的一对组成的“权重”和一个正的数值向量或变量的名称资源描述。该软件对每一行的观测结果进行加权X或资源描述与在相应的值权重。长度权重必须等于行数X或资源描述。

如果将输入数据指定为表资源描述，然后权重可以是变量的名称吗资源描述它包含一个数值向量。在这种情况下，您必须指定权重为一个字符向量或标量的字符串。例如，如果权重向量W存储为Tbl.W，然后将其指定为'W'。否则，软件将处理的所有列资源描述,包括W，作为预测器或训练模型时的响应变量。

默认，权重是一（N，1）,在那里ñ观察到的数量是多少X或资源描述。

该软件可实现权重对相应类中的先验概率值求和。

数据类型：单|双|字符|串

输出参数

全部收缩

`MDL`-训练后的分类模型
分类模型对象

训练分类模型，作为表中分类模型对象之一返回。

学习者名	返回的模型对象
`“discr”`	`CompactClassificationDiscriminant`
`'合奏'`	`CompactClassificationEnsemble`
`'核心'`	`ClassificationKernel`对于二元分类 `CompactClassificationECOC`对于多类分类
`'KNN'`	`ClassificationKNN`
`“线性”`	`ClassificationLinear`对于二元分类 `CompactClassificationECOC`对于多类分类
`“注”`	`CompactClassificationNaiveBayes`
`“支持向量机”`	`CompactClassificationSVM`对于二元分类 `CompactClassificationECOC`对于多类分类
`'树'`	`CompactClassificationTree`

`OptimizationResults`——优化结果
`BayesianOptimization`对象

优化的结果，返回为BayesianOptimization宾语。有关贝叶斯优化过程的详细信息，请参阅贝叶斯优化。

提示

根据您的数据的大小和你指定学习者的数量，fitcauto可能需要一些时间来运行。如果你有一个并行计算工具箱许可，您可以通过运行并行优化加速计算。要做到这一点，指定“HyperparameterOptimizationOptions”、结构(UseParallel,真的)。您可以在结构中包含其他字段，以控制优化的其他方面。看到HyperparameterOptimizationOptions。

算法

全部收缩

自动选择学习者

当您指定“学习者”，“汽车”中，fitcauto函数分析预测和响应数据，以选择合适的学习者。该函数考虑数据集是否具有以下任何特征:

分类预测
丢失超过5%的数据的值
不平衡数据，其中最大类的观测值与最小类的观测值之比大于5
在最小级别超过100个观测
宽数据，其中预测器的数量大于或等于观测值的数目
高维数据，其中预测器的数量大于100
大数据，其中的观察数量大于50,000
二进制响应变量
序响应变量

所选的学习者通常是列表中的子集学习者表格但是，在优化过程中尝试的关联模型对于未优化的超参数可以有不同的默认值，对于优化的超参数可以有不同的搜索范围。

贝叶斯优化

贝叶斯优化，优化总体的目标，是找到最小化的目标函数的一个点。在上下文fitcauto，点是学习者类型和学习者的一组超参数值(参见学习者和OptimizeHyperparameters，目标函数默认情况下是交叉验证分类错误。在…情况下fitcauto，贝叶斯优化在内部保持了一个多TreeBagger模型的目标函数。也就是说，沿着学习型目标函数模型分裂和，对于给定的学习者，该模型是一个TreeBagger回归的合奏。(这个底层模型不同于其他统计和机器学习工具箱函数使用的使用贝叶斯优化的高斯过程模型。)贝叶斯优化通过使用目标函数评估来训练底层模型，并通过使用一个获取函数(“expected-improvement”）。欲了解更多信息，请参阅预期改善。在低采样点之间的采集功能平衡模型的目标函数值并没有得到很好的建模尚未探索的区域。在优化结束，fitcauto选择具有最低目标函数的模型值，优化过程中所考虑的点之间的点。欲了解更多信息，请参阅“标准”、“min-visited-mean”的名称 - 值对参数bestPoint。

另类功能

如果您不确定哪些车型最适合你的数据集，则可以选择使用分类学习使用app，可以对不同的模型进行超参数调优，选择性能最佳的优化模型。尽管在调优模型超参数之前必须选择特定的模型，分类学习为选择可优化超参数和设置超参数值提供了更大的灵活性。但是，您无法并行优化choose“线性”或'核心'学习者，指定观察权重，或在应用中指定先验概率。欲了解更多信息，请参阅超参数优化分类中的应用学习。
如果您知道哪些模型可能适合您的数据，则可以使用相应的模型fit函数并指定'OptimizeHyperparameters'名称 - 值对参数来调谐超参数。您可以比较不同模型的结果来选择最佳的分类。对于本处理的一个例子，请参见使用贝叶斯优化实现模型选择的自动化。

也可以看看

fitcauto

语法

描述

例子

使用表数据自动选择分类器

自动选择分类使用矩阵数据

结合特征选择与自动分类选择

输入参数

资源描述- - - - - -样本数据表

注意

ResponseVarName- - - - - -响应变量名变量的命名资源描述

公式- - - - - -响应变量和预测变量子集的解释模型特征向量|字符串标量

Y- - - - - -类标签数字矢量|分类矢量|逻辑向量|字符数组|字符串数组|字符向量的单元阵列

X- - - - - -预测数据数字矩阵

注意

名称-值对的观点

优化选项

“学习者”- - - - - -分类模型的类型'汽车'(默认)|'所有'|“所有线性”|“all-nonlinear”|一个或多个学习者的名字

'OptimizeHyperparameters'- - - - - -Hyperparameters优化'汽车'(默认)|'所有'

注意

“HyperparameterOptimizationOptions”- - - - - -选择优化结构

分类选项

'CategoricalPredictors'- - - - - -分类预测列表正整数的向量|逻辑向量|汉字字模|字符串数组|字符向量的单元阵列|'所有'

“类名”- - - - - -用于培训的类的名称分类数组|字符数组|字符串数组|逻辑向量|数字矢量|字符向量的单元阵列

“成本”- - - - - -误分类代价方阵|结构数组

'PredictorNames'- - - - - -预测变量名唯一名称的字符串数组|唯一的字符向量的单元阵列

“之前”- - - - - -先验概率'经验'(默认)|“统一”|数字矢量|结构数组

“ResponseName”- - - - - -响应变量名'Y'(默认)|特征向量|字符串标量

'ScoreTransform'- - - - - -分数转换'没有'(默认)|“doublelogit”|“invlogit”|“ismax”|'Logit模型'|功能手柄|……

“权重”- - - - - -观察权重积极的数字矢量|变量的命名资源描述

输出参数

MDL-训练后的分类模型分类模型对象

OptimizationResults——优化结果BayesianOptimization对象

提示

算法

自动选择学习者

贝叶斯优化

另类功能

也可以看看

主题

介绍了在R2020a

统计和机器学习工具箱文档

万博1manbetx

掌握机器学习:用MATLAB逐步指导

`资源描述`- - - - - -样本数据
表

`ResponseVarName`- - - - - -响应变量名
变量的命名`资源描述`

`公式`- - - - - -响应变量和预测变量子集的解释模型
特征向量|字符串标量

`Y`- - - - - -类标签
数字矢量|分类矢量|逻辑向量|字符数组|字符串数组|字符向量的单元阵列

`X`- - - - - -预测数据
数字矩阵

`“学习者”`- - - - - -分类模型的类型
`'汽车'`(默认)|`'所有'`|`“所有线性”`|`“all-nonlinear”`|一个或多个学习者的名字

`'OptimizeHyperparameters'`- - - - - -Hyperparameters优化
`'汽车'`(默认)|`'所有'`

`“HyperparameterOptimizationOptions”`- - - - - -选择优化
结构

`'CategoricalPredictors'`- - - - - -分类预测列表
正整数的向量|逻辑向量|汉字字模|字符串数组|字符向量的单元阵列|`'所有'`

`“类名”`- - - - - -用于培训的类的名称
分类数组|字符数组|字符串数组|逻辑向量|数字矢量|字符向量的单元阵列

`“成本”`- - - - - -误分类代价
方阵|结构数组

`'PredictorNames'`- - - - - -预测变量名
唯一名称的字符串数组|唯一的字符向量的单元阵列

`“之前”`- - - - - -先验概率
`'经验'`(默认)|`“统一”`|数字矢量|结构数组

`“ResponseName”`- - - - - -响应变量名
`'Y'`(默认)|特征向量|字符串标量

`'ScoreTransform'`- - - - - -分数转换
`'没有'`(默认)|`“doublelogit”`|`“invlogit”`|`“ismax”`|`'Logit模型'`|功能手柄|……

`“权重”`- - - - - -观察权重
积极的数字矢量|变量的命名`资源描述`

`MDL`-训练后的分类模型
分类模型对象

`OptimizationResults`——优化结果
`BayesianOptimization`对象