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scikit-learn 1.3.2
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# `sklearn.metrics`.det_curve

sklearn.metrics.det_curve(y_true, y_score, pos_label=None, sample_weight=None)

  Compute error rates for different probability thresholds.

  Note

  This metric is used for evaluation of ranking and error tradeoffs of a binary classification task.

  Read more in the User Guide.

  New in version 0.24.

  Parameters:
    y_true ndarray of shape (n_samples,)

      True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.

    y_score ndarray of shape of (n_samples,)

      Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

    pos_label int, float, bool or str, default=None

      The label of the positive class. When `pos_label=None`, if `y_true` is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an error will be raised.

    sample_weight array-like of shape (n_samples,), default=None

      Sample weights.

  Returns:
    fpr ndarray of shape (n_thresholds,)

      False positive rate (FPR) such that element i is the false positive rate of predictions with score >= thresholds[i]. This is occasionally referred to as false acceptance probability or fall-out.

    fnr ndarray of shape (n_thresholds,)

      False negative rate (FNR) such that element i is the false negative rate of predictions with score >= thresholds[i]. This is occasionally referred to as false rejection or miss rate.

    thresholds ndarray of shape (n_thresholds,)

      Decreasing score values.

  See also

  `DetCurveDisplay.from_estimator`

    Plot DET curve given an estimator and some data.

  `DetCurveDisplay.from_predictions`

    Plot DET curve given the true and predicted labels.

  `DetCurveDisplay`

    DET curve visualization.

  `roc_curve`

    Compute Receiver operating characteristic (ROC) curve.

  `precision_recall_curve`

    Compute precision-recall curve.

  Examples

      >>> import numpy as np
      >>> from sklearn.metrics import det_curve
      >>> y_true = np.array([0, 0, 1, 1])
      >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
      >>> fpr, fnr, thresholds = det_curve(y_true, y_scores)
      >>> fpr
      array([0.5, 0.5, 0. ])
      >>> fnr
      array([0. , 0.5, 0.5])
      >>> thresholds
      array([0.35, 0.4 , 0.8 ])

## Examples using `sklearn.metrics.det_curve`

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve