Automatic variable selection is a powerful technique for simplifying models, reducing overfitting, and improving interpretability. It enables you to efficiently identify the most important predictors from a large set of variables. However, it’s important to recognize that automatic variable selection involves random processes. The same algorithm may select different variables across multiple runs, and different algorithms can yield entirely different selections, even when applied to the same data set. The graph below illustrates how different automatic variable selection methods perform across 200 simulation runs. Each box represents one method, with rows corresponding to variables and columns to simulation runs. Black indicates selected variables, while white indicates excluded ones. 🔹 Stepwise Selection: Highly inconsistent, with patterns that often appear random. Very few variables are consistently selected across all runs. 🔹 Regression Tree: Most variables are rarely selected, with only a small subset chosen consistently across simulations. The small median model size reflects this focused selection. 🔹 Random Forest: Demonstrates improved stability compared to regression trees, with more consistently selected variables, though variability persists for weaker predictors. This method tends to include a broader set of variables compared to regression trees. 🔹 Lasso and Elastic Net: Both methods exhibit relatively stable variable selection, with Elastic Net slightly outperforming Lasso due to its larger model size, allowing for broader inclusion of important variables. While no method achieves perfect consistency, Random Forest, Lasso, and Elastic Net generally provide more stable and reliable variable selection results, whereas Stepwise Selection tends to be the least reliable. I published these results in a working paper back in 2018, but they remain highly relevant today. If you're interested, you can read the full paper here: statistiques.public.lu/dam-a…. If you enjoy insights like these, subscribe to my free email newsletter for regular tips on data science, statistics, Python, and R programming. Further details: statisticsglobe.com/newslett… #datasciencetraining #RStats #VisualAnalytics #datastructure #RStudio