classification
- class aitoolbox.experiment.core_metrics.classification.AccuracyMetric(y_true, y_predicted, positive_class_thresh=0.5)[source]
Bases:
AbstractBaseMetric
Model prediction accuracy
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
positive_class_thresh (float or None) – predicted probability positive class threshold. Set it to None when dealing with multi-class labels.
- class aitoolbox.experiment.core_metrics.classification.ROCAUCMetric(y_true, y_predicted)[source]
Bases:
AbstractBaseMetric
Model prediction ROC-AUC
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
- class aitoolbox.experiment.core_metrics.classification.PrecisionRecallCurveAUCMetric(y_true, y_predicted)[source]
Bases:
AbstractBaseMetric
Model prediction PR-AUC
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
- class aitoolbox.experiment.core_metrics.classification.F1ScoreMetric(y_true, y_predicted, positive_class_thresh=0.5)[source]
Bases:
AbstractBaseMetric
Model prediction F1 score
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
positive_class_thresh (float) – predicted probability positive class threshold
- class aitoolbox.experiment.core_metrics.classification.PrecisionMetric(y_true, y_predicted, positive_class_thresh=0.5)[source]
Bases:
AbstractBaseMetric
Model prediction precision
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
positive_class_thresh (float) – predicted probability positive class threshold
- class aitoolbox.experiment.core_metrics.classification.RecallMetric(y_true, y_predicted, positive_class_thresh=0.5)[source]
Bases:
AbstractBaseMetric
Model prediction recall score
- Parameters:
y_true (numpy.ndarray or list) – ground truth targets
y_predicted (numpy.ndarray or list) – predicted targets
positive_class_thresh (float) – predicted probability positive class threshold