ksfeatureselector 0.1.1

Select features using Kolmogorov–Smirnov (K-S) test for binary classification tasks.

KSFeatureSelector is a lightweight Python package for selecting the most discriminatory features in a binary classification problem using the Kolmogorov–Smirnov (K-S) test.

Features

• Uses the K-S test to rank features by their ability to separate classes.

• Supports filtering features by: - A maximum p-value threshold. - A fixed number of top features.

• Numpy-style docstrings and validations.

• Pure Python using pandas and scipy.

Installation

From PyPI:

pip install ksfeatureselector

Local installation:

pip install .

Usage

from ksfeatureselector import select_ks_features

x_cols = ['feature1', 'feature2', 'feature3']
y_var = 'target'

# Select top features based on p-value or top-n count
select_ks_features(df, x_cols, y_var, top_p=0.05)
# or
select_ks_features(df, x_cols, y_var, top_n=5)

Arguments

• df (pd.DataFrame): The input DataFrame containing feature columns and a binary target column.

• x_cols (List[str]): List of column names in df to be considered as features.

• y_var (str): The name of the target column in df. Must be binary (e.g., 0/1 or True/False).

• top_p (float, optional): Select features whose K-S test p-value is less than this threshold. Use this for statistical significance filtering.

• top_n (int, optional): Select the top N features with the smallest p-values, ranked by their ability to distinguish between the two classes.

Returns

• List[str]: A list of selected feature names based on the K-S test ranking.

License

MIT License

Author

V Subrahmanya Raghu Ram Kishore Parupudi Email: [e-mail]