Predict the training set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.

Load theionospheredata set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b') or good ('g').

loadionosphere

Train a binary kernel classification model that identifies whether the radar return is bad ('b') or good ('g').

rng('default')% For reproducibilityMdl = fitckernel(X,Y);

Mdlis aClassificationKernelmodel.

Predict the training set, or resubstitution, labels.

label = predict(Mdl,X);

Construct a confusion matrix.

ConfusionTrain = confusionchart(Y,label);

The model misclassifies one radar return for each class.

Predict the test set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.

Load theionospheredata set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b') or good ('g').

loadionosphere

Partition the data set into training and test sets. Specify a 15% holdout sample for the test set.

rng('default')% For reproducibilityPartition = cvpartition(Y,'Holdout',0.15); trainingInds = training(Partition);% Indices for the training settestInds = test(Partition);% Indices for the test set

Train a binary kernel classification model using the training set. A good practice is to define the class order.

Mdl = fitckernel(X(trainingInds,:),Y(trainingInds),'ClassNames',{'b','g'});

Predict the training-set labels and the test set labels.

labelTrain = predict(Mdl,X(trainingInds,:)); labelTest = predict(Mdl,X(testInds,:));

Construct a confusion matrix for the training set.

ConfusionTrain = confusionchart(Y(trainingInds),labelTrain);

The model misclassifies only one radar return for each class.

Construct a confusion matrix for the test set.

ConfusionTest = confusionchart(Y(testInds),labelTest);

The model misclassifies one bad radar return as being a good return, and five good radar returns as being bad returns.

Estimate posterior class probabilities for a test set, and determine the quality of the model by plotting a receiver operating characteristic (ROC) curve. Kernel classification models return posterior probabilities for logistic regression learners only.

Load theionospheredata set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b') or good ('g').

loadionosphere

Partition the data set into training and test sets. Specify a 30% holdout sample for the test set.

rng('default')% For reproducibilityPartition = cvpartition(Y,'Holdout',0.30); trainingInds = training(Partition);% Indices for the training settestInds = test(Partition);% Indices for the test set

Train a binary kernel classification model. Fit logistic regression learners.

Mdl = fitckernel(X(trainingInds,:),Y(trainingInds),...'ClassNames',{'b','g'},'Learner','logistic');

Predict the posterior class probabilities for the test set.

[~,posterior] = predict(Mdl,X(testInds,:));

BecauseMdlhas one regularization strength, the outputposterioris a matrix with two columns and rows equal to the number of test-set observations. Columnicontains posterior probabilities ofMdl.ClassNames(i)given a particular observation.

计算性能指标(真正的positive rates and false positive rates) for a ROC curve and find the area under the ROC curve (AUC) value by creating arocmetricsobject.

rocObj = rocmetrics(Y(testInds),posterior,Mdl.ClassNames);

Plot the ROC curve for the second class by using theplotfunction ofrocmetrics.

plot(rocObj,ClassNames=Mdl.ClassNames(2))

The AUC is close to1, which indicates that the model predicts labels well.