Garis besar topik

  • Best Subset Regression is a method in regression analysis used to select the best combination of predictor (independent) variables that are most relevant in predicting the response (dependent) variable. In Best Subset Regression, the model is built by considering all possible combinations of predictor variables, then the best model is selected based on certain criteria, such as Adjusted R-squared, AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), or MSE (Mean Squared Error). This method is very effective in finding the simplest model while still having optimal predictive performance, thus helping to avoid overfitting problems and ensuring that only significant variables are used in the regression model.