plot a violin tree in matlab

To plot a violin tree in MATLAB, we can use the ViolinTree function from the Statistics and Machine Learning Toolbox.

First, we need to load and prepare the data. Let's assume we have a dataset with two predictor variables: X1 and X2, and a binary response variable Y. We can load the dataset and split it into training and test sets as follows:

main.m
load fisheriris
X = meas(:,1:2);
Y = (strcmp(species,'setosa'));
cv = cvpartition(Y,'holdout',0.2);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training);
Xtest = X(cv.test,:);
Ytest = Y(cv.test);
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Next, we can fit a decision tree model to the training data:

main.m
tree = fitctree(Xtrain,Ytrain);
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To plot a violin tree, we need to create a new ViolinTree object from the fitted decision tree:

main.m
vt = ViolinTree(tree);
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We can then use the plot method to visualize the violin tree:

main.m
figure;
vt.plot;
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This will generate a plot with a violin plot for each terminal node in the tree, where the width of each violin corresponds to the number of observations at that node, and the height corresponds to the distribution of the response variable (in this case, Y). The decision tree structure is superimposed on top of the violin plots, with each branch colored according to the majority class at that node.

Note that ViolinTree also supports various optional input arguments for customization of the plot, such as MaxNumBins, LineWidth, and NodeSize. Refer to the MATLAB documentation for more information.