Ensemble learning 101

Ensemble learning in machine learning (ML) draws parallels to the "wisdom of the crowds," where combining predictions from multiple independent models can enhance predictive accuracy. This technique is foundational to methods like random forests and XGBoost, which are popular among practitioners. Ensemble methods are categorized into three main types: stacked models, bagging, and boosting, each achieving model diversity differently to improve performance. Stacked models use a variety of modeling approaches on the same dataset, bagging employs different datasets obtained through bootstrapping to train versions of the same model, and boosting trains models sequentially to correct previous errors. Despite their success in improving predictive performance, ensemble methods often face challenges with explainability, a critical aspect for deploying trustworthy ML systems. Techniques like SHAP and LIME offer post hoc explanations to address this gap, allowing practitioners to balance between performance and interpretability. Understanding the nuances of ensemble methods aids in selecting the appropriate approach for specific ML tasks.