Classification label evaluation

summarize_label_metrics_binary(y_true, y_pred)

Generate a comprehensive report of various evaluation metrics for binary classification results.

The output includes accuracy, precision, recall, F1 scores and confusion matrix elements (true negatives, false positives, false negatives, true positives).

Parameters:

Name	Type	Description	Default
y_true	ndarray	True labels.	required
y_pred	ndarray	Predicted labels. The array should come from a binary classifier.	required

Returns:

Type	Description
Dict[str, Number]	A dictionary containing the evaluated metrics.

Source code in eis_toolkit/evaluation/classification_label_evaluation.py

def summarize_label_metrics_binary(y_true: np.ndarray, y_pred: np.ndarray) -> Dict[str, Number]:
    """
    Generate a comprehensive report of various evaluation metrics for binary classification results.

    The output includes accuracy, precision, recall, F1 scores and confusion matrix elements
    (true negatives, false positives, false negatives, true positives).

    Args:
        y_true: True labels.
        y_pred: Predicted labels. The array should come from a binary classifier.

    Returns:
        A dictionary containing the evaluated metrics.
    """
    metrics = {}

    metrics["Accuracy"] = accuracy_score(y_true, y_pred)

    precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary")
    metrics["Precision"] = precision
    metrics["Recall"] = recall
    metrics["F1_score"] = f1

    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
    metrics["True_negatives"] = tn
    metrics["False_positives"] = fp
    metrics["False_negatives"] = fn
    metrics["True_positives"] = tp

    return metrics