evalml.model_understanding.precision_recall_curve¶
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evalml.model_understanding.precision_recall_curve(y_true, y_pred_proba, pos_label_idx=- 1)[source]¶ Given labels and binary classifier predicted probabilities, compute and return the data representing a precision-recall curve.
- Parameters
y_true (pd.Series or np.ndarray) – True binary labels.
y_pred_proba (pd.Series or np.ndarray) – Predictions from a binary classifier, before thresholding has been applied. Note this should be the predicted probability for the “true” label.
pos_label_idx (int) – the column index corresponding to the positive class. If predicted probabilities are two-dimensional, this will be used to access the probabilities for the positive class.
- Returns
Dictionary containing metrics used to generate a precision-recall plot, with the following keys:
precision: Precision values.
recall: Recall values.
thresholds: Threshold values used to produce the precision and recall.
auc_score: The area under the ROC curve.
- Return type
list