score-regression 0.0.23

Contact

Rolf Carlson hrolfrc@gmail.com

Install

Use pip to install score_regression.

pip install score-regression

Introduction

This is a python implementation of a classifier that maximizes AUC. The idea is to find the features that maximize AUC, analogous to CALF, but relax the requirement that the weights be integers in [-1, 0, 1] and instead allow the weights to be any real number.

ScoreRegression provides classification and prediction for two classes, the binomial case. Small to medium problems are supported. This is research code and a work in progress.

ScoreRegression is designed for use with scikit-learn pipelines and composite estimators.

Example

from score_regression import ScoreRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

seed = 42
X, y = make_classification(
    n_samples=30,
    n_features=5,
    n_informative=2,
    n_redundant=2,
    n_classes=2,
    random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)

cls = ScoreRegression().fit(X_train, y_train)

cls.score(X_test, y_test)

1.0

References

[1] Jeffries, C.D., Ford, J.R., Tilson, J.L. et al. A greedy regression algorithm with coarse weights offers novel advantages. Sci Rep 12, 5440 (2022). https://doi.org/10.1038/s41598-022-09415-2