pr38 / socraticbumpsearch Goto Github PK

A scikit-learn compatible implementation of Bumping as described by “Elements of Statistical Learning” second edition (290-292). As consistent with the rest of scikit-learn, I have used joblib to parallelize the search.

“We draw bootstrap samples and fit a model to each. But rather than average the predictions, we choose the model estimated from a bootstrap sample that best fits the training data. “(Elements of Statistical Learning” second edition, 291)

Bumming seems to be recommended for noisy data sets.

“By perturbing the data, bumping tries to move the fitting procedure around to good areas of model space. For example, if a few data points are causing the procedure to find a poor solution, any bootstrap” (Elements of Statistical Learning” second edition, 291).

pip install git+https://github.com/pr38/socraticbumpsearch