Ensemble Predictions : Combine Model Predictions Into Ensemble Predictions

Many times happens that it take too much time to find well performing machine learning algorithms for your dataset. Trial and error nature of applied machine learning is the reason behind it.

Once we have a selected list of accurate models, we can use algorithm tuning to get the most from each algorithm.

Another approach that we can use to increase accuracy on our dataset is to combine the predictions of multiple different models together.

Combine Model Predictions Into Ensemble Predictions

The three most popular methods for combining the predictions from different models are:

·         Bagging. Building multiple models (typically of the same type) from different subsamples of the training dataset.

·         Boosting. Building multiple models (typically of the same type) each of which learns to fix the prediction errors of a prior model in the chain.

·         Stacking. Building multiple models (typically of differing types) and supervisor model that learns how to best combine the predictions of the primary models.