This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

crossval

Class: ClassificationTree

Cross-validated decision tree

Syntax

cvmodel = crossval(model)
cvmodel = crossval(model,Name,Value)

Description

cvmodel = crossval(model) creates a partitioned model from model, a fitted classification tree. By default, crossval uses 10-fold cross validation on the training data to create cvmodel.

cvmodel = crossval(model,Name,Value) creates a partitioned model with additional options specified by one or more Name,Value pair arguments.

Input Arguments

expand all

model

A classification model, produced using fitctree.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Cross-validation partition, specified as the comma-separated pair consisting of 'CVPartition' and a cvpartition object created by the cvpartition function. crossval splits the data into subsets with cvpartition.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Fraction of the data used for holdout validation, specified as the comma-separated pair consisting of 'Holdout' and a scalar value in the range (0,1).

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'Holdout',0.3

Data Types: single | double

Number of folds to use in a cross-validated model, specified as the comma-separated pair consisting of 'KFold' and a positive integer value greater than 1.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'KFold',3

Data Types: single | double

Leave-one-out cross-validation flag, specified as the comma-separated pair consisting of 'Leaveout' and 'on' or 'off'. Leave-one-out is a special case of 'KFold' in which the number of folds equals the number of observations.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'Leaveout','on'

Output Arguments

expand all

Partitioned model, returned as a ClassificationPartitionedModel object.

Examples

expand all

Create a classification model for the ionosphere data, then create a cross-validation model. Evaluate the quality the model using kfoldLoss.

load ionosphere
tree = fitctree(X,Y);
cvmodel = crossval(tree);
L = kfoldLoss(cvmodel)
L = 0.1111

Tips

  • Assess the predictive performance of model on cross-validated data using the “kfold” methods and properties of cvmodel, such as kfoldLoss.

Alternatives

You can create a cross-validation tree directly from the data, instead of creating a decision tree followed by a cross-validation tree. To do so, include one of these five options in fitctree: 'CrossVal', 'KFold', 'Holdout', 'Leaveout', or 'CVPartition'.

See Also

|