What is Stacking

Stacking is a machine learning ensemble technique that combines predictions from multiple base models (called level-0 models) by training a meta-model (level-1 model) to learn the optimal way to blend their outputs for improved predictive performance. This method involves splitting the training data into folds, training base models on subsets of data, generating predictions on holdout sets, then using these predictions as features to train a meta-learner that makes final predictions. Stacking leverages the strengths of diverse algorithms by allowing the meta-model to learn which base models perform best under different conditions and how to weight their contributions dynamically. The technique typically uses cross-validation to generate out-of-fold predictions, preventing overfitting and ensuring the meta-model learns from unbiased base model outputs. Common implementations include stacking different algorithm types like decision trees, neural networks, and linear models, then using logistic regression or neural networks as meta-learners. Enterprise applications utilize stacking for critical prediction tasks such as fraud detection, risk assessment, and demand forecasting where improved accuracy justifies the additional computational complexity. Stacking often achieves superior performance compared to individual models or simple averaging methods by capturing complex relationships between base model predictions.