evalml.pipelines.roc_curve(y_true, y_pred_proba)[source]

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

  • 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.


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