evalml.model_understanding.roc_curve

evalml.model_understanding.roc_curve(y_true, y_pred_proba)

Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve.

Parameters

• y_true (pd.Series or np.array) – true labels.

• y_pred_proba (pd.Series or np.array) – predictions from a classifier, before thresholding has been applied. Note that 1 dimensional input is expected.

Returns

Dictionary containing metrics used to generate an ROC plot, with the following keys:

• fpr_rate: False positive rate.

• tpr_rate: True positive rate.

• threshold: Threshold values used to produce each pair of true/false positive rates.

• auc_score: The area under the ROC curve.

Return type

dict

evalml.model_understanding.graph_precision_recall_curve evalml.model_understanding.graph_roc_curve