[metrics]
Iterative and federative evaluation metrics computation tools.
This module provides with Metric, an abstract base class that defines an API to iteratively and/or federatively compute evaluation metrics, as well as a number of concrete standard machine learning metrics.
Abstractions
- Metric: Abstract base class defining an API for metrics' computation.
- MeanMetric: Abstract class that defines a template for simple scores' averaging.
Utils
- MetricSet: Wrapper to bind together an ensemble of Metric instances.
- MetricInputType:
Type alias for valid inputs to specify a metric for
MetricSet
. Equivalent toUnion[Metric, str, Tuple[str, Dict[str, Any]]]
.
Classification metrics
- BinaryAccuracyPrecisionRecall: Accuracy, precision, recall and confusion matrix for binary classif. Identifier name: "binary-classif".
- MulticlassAccuracyPrecisionRecall: Accuracy, precision, recall and confusion matrix for multiclass classif. Identifier name: "multi-classif".
- BinaryRocAUC: Receiver Operator Curve and its Area Under the Curve for binary classif. Identifier name: "binary-roc".
Regression metrics
- MeanAbsoluteError: Mean absolute error, averaged across all samples (and channels). Identifier name: "mae".
- MeanSquaredError: Mean squared error, averaged across all samples (and channels). Identifier name: "mse".
- RSquared: R^2 (R-Squared, coefficient of determination) regression metric. Identifier name: "r2".