Closed as not planned
Closed as not planned
Description
Describe the workflow you want to enable
Would it be possible to add a new score to sklearn.metrics, namely the baseline corrected accuracy score (BCAS) (DOI:10.5281/zenodo.15262049). The proposed metric quantifies the model improvement w.r.t. the baseline, and represents a direct evaluation of classifier performance. See the proposed code below, which is label agnostic, and is suitable for both binary and multi-class classification.
Describe your proposed solution
import numpy as np
def BCAS(y_true, y_pred):
"""Baseline corrected accuracy score (BCAS).
Parameters
----------
y_true : Ground truth (correct) labels.
y_pred : Predicted labels.
Returns
-------
score : float
"""
label, count = np.unique(y_true, return_counts=True)
most_frequent_class = label[np.argmax(count)]
y_baseline = np.full(len(y_true), most_frequent_class)
as_baseline = np.mean(y_true == y_baseline)
as_predicted = np.mean(y_true == y_pred)
return (as_predicted - as_baseline)
Describe alternatives you've considered, if relevant
No response
Additional context
No response