Calculate Performance Metrics for a Classification Model

#77 · Machine Learning · Medium

Problem

Calculate multiple performance metrics for a binary classification model: accuracy, precision, recall, and F1 score, given true labels and predicted labels.

Solution

def classification_metrics(y_true, y_pred):
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
    tn = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 0)
    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)

    n = len(y_true)
    accuracy = (tp + tn) / n if n > 0 else 0
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

    return {
        "accuracy": round(accuracy, 4),
        "precision": round(precision, 4),
        "recall": round(recall, 4),
        "f1_score": round(f1, 4)
    }

Explanation

Count TP, TN, FP, FN from the true and predicted label pairs.
Accuracy = (TP + TN) / total: overall correct predictions.
Precision = TP / (TP + FP): of all predicted positives, how many are correct.
Recall = TP / (TP + FN): of all actual positives, how many were found.
F1 Score = harmonic mean of precision and recall, balancing both metrics.

Complexity

Time: O(n) where n is the number of samples
Space: O(1)

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