featboostx

FeatBoost-X

Python implementation of FeatBoost-X. See the paper for details.

Usage

pip install featboostx

Example

from featboostx import FeatBoostClassifier

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

For a more detailed example, see the classification example or the regression example.

Feature selection methods

FeatBoost-X is available classification, regression, and survival problems.

• Classification supports the objectives accuracy (acc) and the F1-score (f1) through the FeatBoostClassifier-class. These can be optimized through the softmax or adaboost objective. This implementation originates from the Python implementation of the original paper.
• Regression supports the mae objective through the FeatBoostRegressor-class and can be optimized through adaptive boosting.
• Survival supports the c_index objective through the FeatBoostRegressor-class and can be optimized through adaptive boosting.

Illustration of FeatBoost-X