shapboost 1.0.0

A Python package for the SHAPBoost feature selection algorithm

SHAPBoost

Python implementation of SHAPBoost. See the paper for details.

Usage

pip install shapboost

Example

from shapboost import SHAPBoostRegressor

clf = SHAPBoostRegressor()
clf.fit(X, y)
print(clf.selected_subset_)

For a more detailed example, see the regression example.

Feature selection methods

SHAPBoost is available for regression, and survival problems.

• Regression supports the mae, mse, and r2 objectives through the SHAPBoostRegressor-class and can be optimized through adaptive boosting.
• Survival supports the c_index objective through the SHAPBoostRegressor-class and can be optimized through adaptive boosting.

Important notes

• The estimator hyperparameter sets the estimators used for the SISO- and MISO steps, and for the updating of the sample weights (or the boosting), the first estimator is used. Thus, this first estimator needs to be a tree model that supports the sample_weight parameter.

Illustration of SHAPBoost