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Metrics

Evaluating the performance of a machine learning model is crucial for understanding its effectiveness and pinpointing areas for improvement. sorix provides a variety of standard metrics to evaluate models across different tasks.

Regression Metrics

  • Regression: Metrics for assessing continuous predictions. Includes MAE, MSE, RMSE, and R2.

Classification Metrics

  • Classification: Metrics for categorical predictions. Features Accuracy, Precision, Recall, and F1 Score.

Threshold Calibration

  • Optimal Classification Threshold: Automatically find the decision boundary that maximises a given metric (F1, accuracy, or any custom function) on a validation set.

All metrics are designed to work seamlessly with sorix tensors and support both scalar and batch computations.