Simple evaluation of classification confidence intervals.
pip install git+https://github.com/g8a9/confidence_intervals.git
clf = RandomForestClassifier().fit(X_train, y_train)
y_pred = clf.predict(X_test)
bootstrap_test(y_test, y_pred)
# Output
>>> ConfidenceResult(n_iter=200, ci_level=95, mean=0.9777111111111112, ci_lower=0.9643888888888889, ci_upper=0.9888888888888889)
Confidence Intervals Estimation by means of:
- bootstrap sampling on test set predictions (use
n_iter > 200
) - simple estimation over multiple runs (useful for CI estimation across multiple random initializations)
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.