What are common ML evaluation metrics?

In machine learning (ML), evaluation metrics are essential for assessing how well a model performs. Choosing the right metric depends on the type of problem—classification, regression, or clustering.

For classification tasks, some widely used metrics include:

Accuracy: The ratio of correctly predicted instances to the total instances. It works well when classes are balanced.

Precision: Measures how many of the predicted positives are truly positive. It’s useful when the cost of false positives is high.

Recall (Sensitivity): Tells how many actual positives were correctly predicted. Ideal when missing a positive case is critical.

F1-Score: The harmonic mean of precision and recall. It balances both and is ideal when classes are imbalanced.

ROC-AUC: Represents the model’s ability to distinguish between classes. AUC near 1.0 means excellent classification.

For regression tasks, metrics include:

Mean Absolute Error (MAE): The average absolute difference between actual and predicted values.

Mean Squared Error (MSE): Similar to MAE but penalizes larger errors more heavily.

Root Mean Squared Error (RMSE): The square root of MSE; it gives error in the same unit as the output.

R² Score (Coefficient of Determination): Explains the proportion of variance in the target variable that’s predictable from the input features.

In clustering problems, metrics like Silhouette Score and Davies-Bouldin Index help measure how well data points fit within clusters.

Choosing the right evaluation metric helps you build more effective models by highlighting strengths and weaknesses in performance. A clear understanding of these metrics is essential for making data-driven decisions in real-world applications.

To explore these concepts in depth, consider enrolling in a data science and machine learning course that covers practical model evaluation techniques.

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