Machine learning metrics are essential for evaluating the performance of machine learning models. These metrics help us understand how well our models are performing and where they may need improvement.

Common Metrics

  • Accuracy: The percentage of correctly predicted instances.

    • Formula: ( \text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Instances}} )

  • Precision: The ratio of correctly predicted positive observations to the total predicted positives.

    • Formula: ( \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}} )

  • Recall: The ratio of correctly predicted positive observations to all actual positives.

    • Formula: ( \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} )

  • F1 Score: The weighted average of Precision and Recall.

    • Formula: ( \text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} )

Evaluation Metrics for Different Tasks

  • Classification Tasks: Accuracy, Precision, Recall, F1 Score, ROC-AUC.
  • Regression Tasks: Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared.

Machine Learning Metrics

For more information on machine learning metrics, you can visit our Machine Learning Basics page.