data = readtable('ML2.xlsx');  


X = data{:, {'WCP', 'NaOH', 'W', 'SS', 'Age'}};  
y = data.UCS;                                   

%% Train GBM
gbModel = fitrensemble(X, y, ...
    'Method', 'LSBoost', ...            
    'NumLearningCycles', 500, ...        
    'LearnRate', 0.05, ...               
    'Learners', templateTree('MaxNumSplits', 20));  


y_pred = predict(gbModel, X);

%% Evaluate Model
r2 = 1 - sum((y - y_pred).^2) / sum((y - mean(y)).^2);
rmse = sqrt(mean((y - y_pred).^2));
mae = mean(abs(y - y_pred));
mape = mean(abs((y - y_pred) ./ y)) * 100;

fprintf('Gradient Boosting Performance (Full Data):\n');
fprintf('  R² = %.4f\n', r2);
fprintf('  RMSE = %.2f kPa\n', rmse);
fprintf('  MAE = %.2f kPa\n', mae);
fprintf('  MAPE = %.2f %%\n', mape);