Скачать или смотреть Confusion Matrix and Precision, Recall - Machine Learning

The sources detail the critical concepts used for evaluating machine learning classifiers, moving beyond simple accuracy.
1. Classification models require careful evaluation, utilizing the confusion matrix to decompose predictions into True Positives, False Positives, True Negatives, and False Negatives.
2. Precision measures how correct a model's positive predictions are, important when False Positives are consequential (e.g., spam detection).
3. Recall measures how many actual positive instances were correctly identified, crucial when False Negatives are critical (e.g., cancer detection).
4. A common tradeoff exists between these two metrics, requiring developers to decide which error type (False Positive or False Negative) is less acceptable for the problem being solved.
5. The F1-Score is a single performance metric that takes the harmonic mean of both precision and recall, providing a balanced measure often used to rank different models.