Ensemble Techniques in Machine Learning
Ensemble Techniques in Machine Learning Ensemble techniques in machine learning involve combining multiple individual models to create a more powerful and accurate model. The basic idea behind ensemble methods is that by combining the predictions of multiple models, you can often achieve better performance than with a single model. Ensembles are widely used in machine learning because they can improve model robustness, reduce overfitting, and enhance predictive accuracy. There are several popular ensemble techniques, including: Bagging (Bootstrap Aggregating): Bagging involves training multiple instances of the same model on different subsets of the training data, often with replacement. The final prediction is obtained by averaging or taking a majority vote of the predictions from these individual models. Random Forest is a well-known ensemble method that uses bagging with decision trees. Boosting: Boosting methods focus on sequentially tra...
