resubPredict

Predict resubstitution labels of classification tree

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Syntax

label = resubPredict(tree) [label,posterior] = resubPredict(tree) [label,posterior,node] = resubPredict(tree) [label,posterior,node,cnum] = resubPredict(tree) [label,...] = resubPredict(tree,Name,Value)

Description

label= resubPredict(tree)returns the labelstreepredicts for the datatree.X.labelis the predictions oftreeon the data thatfitctreeused to createtree.

[label,posterior] = resubPredict(tree)returns the posterior class probabilities for the predictions.

[label,posterior,node] = resubPredict(tree)returns the node numbers oftreefor the resubstituted data.

[label,posterior,node,cnum] = resubPredict(tree)returns the predicted class numbers for the predictions.

[label,...] = resubPredict(tree,Name,Value)returns resubstitution predictions with additional options specified by one or moreName,Valuepair arguments.

Input Arguments

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tree

A classification tree constructed byfitctree.

Name-Value Arguments

Specify optional comma-separated pairs ofName,Valuearguments.Nameis the argument name andValueis the corresponding value.Namemust appear inside quotes. You can specify several name and value pair arguments in any order asName1,Value1,...,NameN,ValueN.

`Subtrees`—Pruning level
0(default) |vector of nonnegative integers|`'all'`

Pruning level, specified as the comma-separated pair consisting of'Subtrees'and a vector of nonnegative integers in ascending order or'all'.

If you specify a vector, then all elements must be at least0and at mostmax(tree.PruneList).0indicates the full, unpruned tree andmax(tree.PruneList)indicates the completely pruned tree (i.e., just the root node).

If you specify'all', thenresubPredictoperates on all subtrees (i.e., the entire pruning sequence). This specification is equivalent to using0:max(tree.PruneList).

resubPredictprunestreeto each level indicated inSubtrees, and then estimates the corresponding output arguments. The size ofSubtreesdetermines the size of some output arguments.

To invokeSubtrees, the propertiesPruneListandPruneAlphaoftreemust be nonempty. In other words, growtreeby setting'Prune','on', or by pruningtreeusingprune.

Example:'Subtrees','all'

Data Types:single|double|char|string

Output Arguments

`label`	The response`tree`predicts for the training data.`label`is the same data type as the training response data`tree.Y`. If the`Subtrees`name-value argument contains`m`>`1`entries,`label`has`m`columns, each of which represents the predictions of the corresponding subtree. Otherwise,`label`is a vector.
`posterior`	Matrix or array of posterior probabilities for classes`tree`predicts. If the`Subtrees`name-value argument is a scalar or is missing,`posterior`is an`n`-by-`k`matrix, where`n`的行数在吗the training data`tree.X`, and`k`is the number of classes. If`Subtrees`contains`m`>`1`entries,`posterior`is an`n`-by-`k`-by-`m`array, where the matrix for each`m`gives posterior probabilities for the corresponding subtree.
`node`	The node numbers of`tree`where each data row resolves. If the`Subtrees`name-value argument is a scalar or is missing,`node`is a numeric column vector with`n`rows, the same number of rows as`tree.X`. If`Subtrees`contains`m`>`1`entries,`node`is a`n`-by-`m`matrix. Each column represents the node predictions of the corresponding subtree.
`cnum`	The class numbers that`tree`predicts for the resubstituted data. If the`Subtrees`name-value argument is a scalar or is missing,`cnum`is a numeric column vector with`n`rows, the same number of rows as`tree.X`. If`Subtrees`contains`m`>`1`entries,`cnum`is a`n`-by-`m`matrix. Each column represents the class predictions of the corresponding subtree.

Examples

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Compute Number of Misclassified Observations

Open Live Script

Find the total number of misclassifications of the Fisher iris data for a classification tree.

loadfisheriristree = fitctree(meas,species); Ypredict = resubPredict(tree);% The predictionsYsame = strcmp(Ypredict,species);% True when ==sum(~Ysame)% How many are different?

ans = 3

Compare In-Sample Posterior Probabilities for Each Subtree

Open Live Script

Load Fisher's iris data set. Partition the data into training (50%)

loadfisheriris

Grow a classification tree using the all petal measurements.

Mdl = fitctree(meas(:,3:4),species); n = size(meas,1);% Sample sizeK = numel(Mdl.ClassNames);% Number of classes

View the classification tree.

view(Mdl,“模式”,'graph');

Figure Classification tree viewer contains an axes object and other objects of type uimenu, uicontrol. The axes object contains 18 objects of type line, text.

The classification tree has four pruning levels. Level 0 is the full, unpruned tree (as displayed). Level 4 is just the root node (i.e., no splits).

Estimate the posterior probabilities for each class using the subtrees pruned to levels 1 and 3.

[~,Posterior] = resubPredict(Mdl,'SubTrees',[1 3]);

Posterioris ann-by-K-by- 2 array of posterior probabilities. Rows ofPosteriorcorrespond to observations, columns correspond to the classes with orderMdl.ClassNames, and pages correspond to pruning level.

Display the class posterior probabilities for iris 125 using each subtree.

Posterior(125,:,:)

ans = ans(:,:,1) = 0 0.0217 0.9783 ans(:,:,2) = 0 0.5000 0.5000

The decision stump (page 2 ofPosterior) has trouble predicting whether iris 125 is versicolor or virginica.

More About

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Posterior Probability

The posterior probability of the classification at a node is the number of training sequences that lead to that node with this classification, divided by the number of training sequences that lead to that node.

For example, consider classifying a predictorXastruewhenX<0.15orX>0.95, andXis false otherwise.

Generate 100 random points and classify them:

rng(0)% For reproducibilityX = rand(100,1); Y = (abs(X - .55) > .4); tree = fitctree(X,Y); view(tree,“模式”,'graph')

Figure Classification tree viewer contains an axes object and other objects of type uimenu, uicontrol. The axes object contains 12 objects of type line, text.

Prune the tree:
```
tree1 = prune(tree,'Level',1); view(tree1,“模式”,'graph')
```
The pruned tree correctly classifies observations that are less than 0.15 astrue. It also correctly classifies observations between .15 and .94 asfalse. However, it incorrectly classifies observations that are greater than .94 asfalse. Therefore the score for observations that are greater than .15 should be about .05/.85=.06 fortrue, and about .8/.85=.94 forfalse.

Compute the prediction scores for the first 10 rows ofX:

[~,score] = predict(tree1,X(1:10)); [score X(1:10,:)]

ans =10×30.9059 0.0941 0.8147 0.9059 0.0941 0.9058 0 1.0000 0.1270 0.9059 0.0941 0.9134 0.9059 0.0941 0.6324 0 1.0000 0.0975 0.9059 0.0941 0.2785 0.9059 0.0941 0.5469 0.9059 0.0941 0.9575 0.9059 0.0941 0.9649

Indeed, every value ofX(the rightmost column) that is less than 0.15 has associated scores (the left and center columns) of0and1, while the other values ofXhave associated scores of0.94and0.06.

Extended Capabilities

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, seeRun MATLAB Functions on a GPU(Parallel Computing Toolbox).

resubPredict

Syntax

Description

Input Arguments

Name-Value Arguments

`Subtrees`—Pruning level
0(default) |vector of nonnegative integers|`'all'`

Output Arguments

Examples

Compute Number of Misclassified Observations

Compare In-Sample Posterior Probabilities for Each Subtree

More About

Posterior Probability

Extended Capabilities

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

See Also

统计和机器学习Toolbox Documentation

万博1manbetx

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

resubPredict

Syntax

Description

Input Arguments

Name-Value Arguments

Subtrees—Pruning level0(default) |vector of nonnegative integers|'all'

Output Arguments

Examples

Compute Number of Misclassified Observations

Compare In-Sample Posterior Probabilities for Each Subtree

More About

Posterior Probability

Extended Capabilities

GPU ArraysAccelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

See Also

统计和机器学习Toolbox Documentation

万博1manbetx

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

`Subtrees`—Pruning level
0(default) |vector of nonnegative integers|`'all'`

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.