MATLAB: How to run the regression tree examle

fitrtreeregression

I tried to run the basic regression tree example in Matlab, I got the error message:

Undefined variable "RegressionTree" or class "RegressionTree.template".

Error in fitrtree (line 140) temp = RegressionTree.template(varargin{:});

Error in Testfile (line 6) tree = fitrtree([Weight, Cylinders],MPG,…

The code is from the Documentation "fitrtree" first example (<http://www.mathworks.com/help/stats/fitrtree.html)>, which is :

load carsmall;

tree = fitrtree([Weight, Cylinders],MPG,… 'categoricalpredictors',2,'MinParentSize',20,… 'PredictorNames',{'W','C'})

Best Answer

Strange. That code runs fine for me.

I assume you have the Statistics and Machine Learning Toolbox, otherwise you would have just gotten an error that fitrtree could not be found. Can you just confirm that, though? Do you see that toolbox when you type

ver

at the command line? Also, what does

which -all fitrtree

give as output?

Related Solutions

MATLAB: What is ‘categorical predictor’ in decision tree for regression

Matlab's fitrtree() function returns a regression tree object. Read more about this object and its properties here:

https://www.mathworks.com/help/stats/classreg.learning.partition.regressionpartitionedmodel-class.html

As you'll read in the link above, the "CategoricalPredictors" contains index values corresponding to the columns of the categorical predictor data (if none of the predictors are categorical, this will be empty []).

So, why is it CategoricalPredictors equal to 2?

Now read about the function you're using fitrtree()

https://www.mathworks.com/help/stats/fitrtree.html#bt6cr84-tree

One of the name-value pairs (<- link) is 'CategoricalPredictors' which, is specified in your call to fitrtree() as 2. That's because you have two predictors being treated as categorical variables, [Weight, Cylinders].

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: What is ‘categorical predictor’ in decision tree for regression

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

Related Question