MATLAB: Predict does not work with a crossvalidated tree

machine learningpredictregressiontreeStatistics and Machine Learning Toolboxstatistics toolbox

Hey, I am trying to do the following: rtree = RegressionTree.fit(X,Y,'crossval','on'); pred = predict(rtree,X)

But unfortunately I get a error message.

Does anyone know what I can use instead of pred?

Thank you!

Best Answer

When you use the 'crossval' 'on' flag you get a partitioned model as an output. You can predict from this model using

kfoldpredict

http://www.mathworks.com/help/stats/classificationpartitionedmodel.kfoldpredict.html

Related Solutions

MATLAB: Crossval and regressiontree.fit – how does crossval works

What does KFold 10 means?

https://en.wikipedia.org/wiki/Cross-validation_(statistics)

matlab has done exactly?

MATLAB has performed kfold cross-validation and returned a partitioned model instead of a single regression tree. Crossvalidation allows you to assess the predictive performance of model on cross-validated data. You can access these methods using the output of a cross validated model: kfoldfun, kfoldLoss, kfoldPredict.

Alternatively you can fit a model and then call cross val this way:

rtree = RegressionTree.fit(X,price);
rcrossvaltree = crossval(rtree);

Read more here (also includes examples):

http://www.mathworks.com/help/stats/regressionpartitionedmodel-class.html

http://www.mathworks.com/help/stats/regressiontree.crossval.html

MATLAB: How to use the “predictorImportance” function with models generated by applying “crossval” to RegressionTree objects

It is important to note that ‘predictorImportance’ can only be applied to one model at a time. The result of using the “crossval” function on a regression tree will be a set of regression models and thus you will need to index into each of these models to determine the predictor importance.

The following example code which you can execute in the MATLAB command window shows how you can call ‘predictorImportance’ on the results of ‘crossval’

%%Load the sample data.
load carsmall; 
%%Construct a regression tree using the sample data.
tree = fitrtree([Weight, Cylinders],MPG,...
                'categoricalpredictors',2,'MinParentSize',20,...
                'PredictorNames',{'W','C'})  ;
%%cross validation
Ctree = crossval(tree);
% where tree is the original ‘RegressionTree’ object. Note that the default number of folds i.e. regression models generated will be 10
%%Calling predictorImportance on individual regression models
predictorImportance(Ctree.Trained{1})
predictorImportance(Ctree.Trained{10})
% where 1 and 10 represent the indices of the trained models. This would range from 1 to 10 by default

For more information on how to use the ‘fitrtree’ function please refer to the following link:

https://www.mathworks.com/help/stats/fitrtree.html

Best Answer

Related Solutions

MATLAB: Crossval and regressiontree.fit – how does crossval works

MATLAB: How to use the “predictorImportance” function with models generated by applying “crossval” to RegressionTree objects

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