Metrics¶
The metrics module provides evaluation metrics for assessing model performance, split across two
registries by problem type. The regression_metric_registry contains accuracy metrics
(e.g., Spearman correlation, MSE, Pearson correlation) and uncertainty calibration metrics
(e.g., calibration error, coverage, width). The classification_metric_registry contains
metrics for binary and multiclass tasks (accuracy, F1, precision, recall, AUC-ROC).
Results automatically selects the appropriate registry
based on the dataset’s ProblemType.
All metrics are registered at import time and accessible via the global registry instances
exported from the top-level alf_core.utils.metrics namespace. Two further modules sit
outside the registries: acquisition batch metrics, which operate on candidate objects rather
than prediction arrays, and design task metrics, which aggregate per-round values into
end-of-experiment summary scores.