Ensemble Methods
Ensemble methods are machine learning techniques that combine multiple models to improve predictive performance, robustness, and generalization compared to using a single model. They work by aggregating predictions from various base learners, such as decision trees or neural networks, to produce a final output. Common approaches include bagging, boosting, and stacking, which reduce variance, bias, or both in model predictions.
Developers should learn ensemble methods when building machine learning systems that require high accuracy and stability, such as in classification, regression, or anomaly detection tasks. They are particularly useful in competitions like Kaggle, where top-performing solutions often rely on ensembles, and in real-world applications like fraud detection or medical diagnosis where reliability is critical. By leveraging multiple models, ensembles can handle complex data patterns and mitigate overfitting.