Machine Learning Metrics are crucial for evaluating the performance of machine learning models. Here is a brief overview of some commonly used metrics.

  • Accuracy: The ratio of correctly predicted observations to the total observations.
  • Precision: The ratio of correctly predicted positive observations to the total predicted positive observations.
  • Recall: The ratio of correctly predicted positive observations to the all observations in actual class.
  • F1 Score: The weighted average of Precision and Recall.

For more detailed information about these metrics, you can check out our Machine Learning Basics guide.

Metrics in Different Contexts

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

Metrics for Time Series

  • Mean Absolute Scaled Error (MASE): A metric for time series forecasting.
  • Mean Absolute Percentage Error (MAPE): A metric for percentage errors in time series forecasting.

Machine Learning Metrics

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