Model Stacking
Model stacking, also known as stacked generalization, is an ensemble machine learning technique that combines multiple base models (e.g., decision trees, neural networks) by training a meta-model to make final predictions based on their outputs. It typically involves splitting data into training and validation sets, training base models on the training set, generating predictions on the validation set, and using these predictions as features to train the meta-model. This approach aims to improve predictive performance by leveraging the strengths of diverse models and reducing overfitting.
Developers should learn model stacking when working on complex predictive tasks where single models underperform, such as in Kaggle competitions, financial forecasting, or medical diagnosis, as it often achieves higher accuracy and robustness. It is particularly useful in scenarios with heterogeneous data or when base models have complementary error patterns, allowing the meta-model to correct individual weaknesses. However, it requires careful implementation to avoid overfitting and increased computational cost.