why mse is 0.00 for three different data sets?

Please let me know what is wrong in matalb code given below because it is giving mean square error is for three different data sets,

clear, clc, close all

data = readtable('c:/matlab/study_data.csv');

X = data(:, 1:end-1); % Select all columns except the last one

y = data(:, end); % Select the last column

numGroundTruth = numel(y);

numTrainingSamples = round(0.8 * numGroundTruth);

trainingIndexes = randsample(numGroundTruth, numTrainingSamples);

testIndexes = setdiff((1:numGroundTruth)', trainingIndexes);

X_train = X(trainingIndexes, :);

X_test = X(testIndexes, :);

y_train = y(trainingIndexes, :);

y_test = y(testIndexes, :);

% Create a Random Forest classifier

rf_classifier = TreeBagger(100, table2array(X_train), table2array(y_train), 'OOBPrediction', 'On');

predicted = predict(rf_classifier, table2array(X_train));

YY = categorical(predicted);

ZZ = str2double(cellstr(YY));

Z = table2array(y_train);

oob_mse = immse(Z, ZZ);

disp(sprintf('Out-of-Bag Mean Square Error: %.4f', oob_mse));

Thanks for your kind help.

Sanchit

Answer 1

[Menika]

Hi,

A possible problem with the above code can be that the predicted variable is being calculated using the training data X_train, rather than the test data X_test. Since the MSE is calculated using the training data, it will always be zero because the model is predicting the same data it was trained on. You can try replacing X_train with X_test when calculating the predicted variable.

Hope it helps!