Episodic Memory

Ensemble Methods in machine learning involve combining multiple models to improve overall predictive accuracy and robustness. By integrating diverse algorithms—such as decision trees, neural networks, or regressors—these methods reduce individual biases and variances. Common techniques include bagging, boosting, and stacking, enabling the ensemble to achieve better performance than any single constituent model.