Ensembling

Ensemble learning is a machine learning technique that enhances prediction accuracy and robustness by combining the outputs of multiple models.

Stacking Ensembling technique

1. Classification Ensemble - A classification model that combines the predictions of multiple other models using voting techniques, namely hard voting and soft voting. In hard voting, the class that receives the majority of the votes among all the models is selected for the outcome, while soft voting takes the probabilities of each class prediction by all the base models and the class with the highest average probability is selected as the final prediction.
2. Regression Ensemble - A regression model that combines the predictions of multiple other models by averaging techniques, namely weighted average and non-weighted average.
3. Stacking Classification - An ensemble learning classifier that combines the predictions of multiple base classifiers (or models) to create a more powerful and robust meta-classifier. This is particularly useful when you have several diverse classifiers with complementary strengths and weaknesses.
4. Stacking Regression - An ensemble learning regressor that combines the predictions of multiple base regressors (or models) to create a more powerful and robust meta-regressor. This is particularly useful when you have several diverse regressors with complementary strengths and weaknesses.