ROC Curve API¶
-
class
datarobot.models.roc_curve.
RocCurve
(source, roc_points, negative_class_predictions, positive_class_predictions)¶ ROC curve data for model.
Attributes
source (str) ROC curve data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’. roc_points (list of dict) List of precalculated metrics associated with thresholds for ROC curve. negative_class_predictions (list of float) List of predictions from example for negative class positive_class_predictions (list of float) List of predictions from example for positive class -
estimate_threshold
(threshold)¶ Return metrics estimation for given threshold.
Parameters: threshold : float from [0, 1] interval
Threshold we want estimation for
Returns: dict
Dictionary of estimated metrics in form of {metric_name: metric_value}. Metrics are ‘accuracy’, ‘f1_score’, ‘false_negative_score’, ‘true_negative_score’, ‘true_negative_rate’, ‘matthews_correlation_coefficient’, ‘true_positive_score’, ‘positive_predictive_value’, ‘false_positive_score’, ‘false_positive_rate’, ‘negative_predictive_value’, ‘true_positive_rate’.
Raises: ValueError
Given threshold isn’t from [0, 1] interval
-
get_best_f1_threshold
()¶ Return value of threshold that corresponds to max F1 score. This is threshold that will be preselected in DataRobot when you open “ROC curve” tab.
Returns: float
Threhold with best F1 score.
-