Dynamic Ensemble Selection (DES) for Classification in Python
Dynamic ensemble selection is an ensemble learning technique that automatically selects a subset of ensemble members just-in-time when making a prediction.
The technique involves fitting multiple machine learning models on the training dataset, then selecting the models that are expected to perform best when making a prediction for a specific new example, based on the 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 models that perform the best on the neighborhood to make a prediction for the new example.
As such, the