One problem is that you are using the wrong kind of brackets to access table content:
- {} curly braces refer to the table content
- () parentheses refer to the table itself.
Note that MATLAB does not have a function named MEAN_SQUARED_ERROR: do you have this function saved on your MATLAB search path (e.g. did you write it yourself or obtain it from someone else?)
data = readtable('BreastCancerData.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 * numel(y))
trainingIndexes = randsample(numel(y), 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, X_train, y_train, 'OOBPrediction', 'On');
% Print the calculated metrics
oob_error = rf_classifier.oobError
oob_mse = mean_squared_error(y_train, predict(rf_classifier, X_train));
oob_classification_error = 1 - rf_classifier.OOBPermutedPredictorDeltaError(end);
train_accuracy = 1 - oob_error;
test_accuracy = 1 - loss(rf_classifier, X_test, y_test);
disp(['Out-of-Bag Mean Error: ', num2str(oob_error)]);
disp(['Out-of-Bag Mean Squared Error: ', num2str(oob_mse)]);
disp(['Out-of-Bag Classification Error: ', num2str(oob_classification_error)]);
disp(['Training Accuracy: ', num2str(train_accuracy)]);
disp(['Testing Accuracy: ', num2str(test_accuracy)]);
