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How to set tree arguments in TreeBagger

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Fabian
Fabian il 4 Mag 2018
Modificato: luis barbosa il 17 Nov 2018
Hi.
I'm using TreeBagger for classification and I want to control the depth of the trees in it.
I have read that you can control the depth of the trees using tree parameters, MinLeafSize, MinParentSize and MinParentSize in here https://la.mathworks.com/help/stats/fitctree.html#bt6cr7t_sep_shared-MaxNumSplits.
Also in the TreeBagger documentation says that it has an argument TreeArguments that receive a Cell array of arguments for fitctree or fitrtree. These arguments are used by TreeBagger when growing new trees for the ensemble.
I'm trying to set this property but I get the next error with the next code.
maxNumSplits = 3; %Maximal number of decision splits, positive integer
minLeafSize = 3; %Mini um number of leaf node observations, positive integer
minParentSize = 3; %Minimum number of brach node observation, positive integer
numberPredictorsToSample = 3; %number of random feature for each decision, default = squareroot of the total number of features
numTrees = 500; %Scalar value equal to the number of desicion trees
treeArguments = {'MaxNumSplits',maxNumSplits,'MinLeafSize',minLeafSize,'MinParentSize',minParentSize};
RF = TreeBagger(numTrees,trainingData,trainingClass,'TreeArguments',treeArguments);
The Error I get is
Error using internal.stats.parseArgs (line 42) Invalid parameter name: TreeArguments.
Error in classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 115) [W,predictornames,responsename,catpreds] = ...
Error in ClassificationTree.prepareData (line 471) [X,Y,W,dataSummary] = ...
Error in TreeBagger/init (line 1190) [bagger.X,y,bagger.W,bagger.DataSummary,classSummary] = ...
Error in TreeBagger (line 531) bagger = init(bagger,X,Y,makeArgs{:});
Error in variabilidad (line 42) RF = TreeBagger(numTrees,trainingData,trainingClass,'TreeArguments',templateTree);

Risposte (1)

luis barbosa
luis barbosa il 17 Nov 2018
Modificato: luis barbosa il 17 Nov 2018
Dear Fabian,
I saw your code and there are some errors there.
First, the TreeBagger (function/class) does not accept as argument 'TreeArguments'. So it is not possible to creat a cell-array (the case of your code), or a structure and simple pass it to the Trebagger as an argument.
One way to run your code is the following:
maxNumSplits = 3; %Maximal number of decision splits, positive integer
minLeafSize = 3; %Mini um number of leaf node observations, positive integer
minParentSize = 3; %Minimum number of brach node observation, positive integer
numberPredictorsToSample = 3; %number of random feature for each decision, default = squareroot of the total number of features
numTrees = 500; %Scalar value equal to the number of desicion trees
method = 'regression'; %or 'classification'
%% training a TreeBagger.
% It is a random forests since the numberPredictorsToSample is less
% than the amount of inputs in the trainingData, used to train the RF.
% Otherwise, we have a simple bagged ensemble of trees.
% assuming:
% - trainingData is a table
% - y_label is a string with the output name
RF = TreeBagger(numTrees,trainingData,y_label,...
'Method',method,... %you have to say whether it is a regression or classification problem
'MaxNumSplits',maxNumSplits,...
'MinLeafSize',minLeafSize,...
'NumPredictorsToSample',numberPredictorsToSample);
I left out the 'MinParentSize', because an error occurred, namely:
"Error using TreeBagger/init (line 1414)
TreeBagger does not accept 'MinParentSize' and 'SplitMin' input parameters. Use 'MinLeafSize'."
I didn't try to solve this problem. Perhaps, a possible solution is to set this parameter by using templateTree, and pass it to TreeBagger, something like this:
t = templateTree('MinParentSize',number);
RF = TreeBagger(numTrees,trainingData,y_label,...
'Method',method,... %you have to say whether it is a regression or classification problem
'MaxNumSplits',maxNumSplits,...
'MinLeafSize',minLeafSize,...
'NumPredictorsToSample',numberPredictorsToSample,...
'Learners',t); %passing the created templateTree object

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