Dynamic Classifier Selection Ensembles in Python

Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling.

The technique involves fitting multiple machine learning models on the training dataset, then selecting the model that is expected to perform best when making a prediction, based on the specific details of the example to be predicted.

This can be achieved using a k-nearest neighbor model to locate examples in the training dataset that are closest to the new example to be predicted, evaluating all models in the pool on this neighborhood and using the model that performs the best on the neighborhood to make a prediction for the new example.

As such, the dynamic classifier selection can often perform better than any single model



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