Usage

Quick Example

from ml_eval_robust.metrics import ClassificationMetrics
cm = ClassifMetricsCalc()
y_true = [1, 0, 1, 1, 0]
y_pred = [1, 0, 1, 0, 0]
print("Accuracy:", cm.accuracy_calc(y_true, y_pred))
print("Precision:", cm.precision_calc(y_true, y_pred))
print("Recall:", cm.recall_calc(y_true, y_pred))
print("F1 Score:", cm.f1_score_calc(y_true, y_pred))
conf_matrix = cm.confusion_matrix_(y_true, y_pred)
print("Confusion Matrix:", conf_matrix)

=== Classification Metrics ===
Accuracy: 0.8
Precision: 0.999999995
Recall: 0.6666666644444444
F1 Score: 0.799999992
Confusion Matrix: [[2, 0], [1, 2]]

See the API Reference for all available classes and methods.