why mse is 0.00 for three different data sets?
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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
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Menika
am 10 Jul. 2023
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!
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