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

sklearn.metrics.auc(x, y)

  Compute Area Under the Curve (AUC) using the trapezoidal rule.

  This is a general function, given points on a curve. For computing the area under the ROC-curve, see `roc_auc_score`. For an alternative way to summarize a precision-recall curve, see `average_precision_score`.

  Parameters:
    x array-like of shape (n,)

      X coordinates. These must be either monotonic increasing or monotonic decreasing.

    y array-like of shape (n,)

      Y coordinates.

  Returns:
    auc float

      Area Under the Curve.

  See also

  `roc_auc_score`

    Compute the area under the ROC curve.

  `average_precision_score`

    Compute average precision from prediction scores.

  `precision_recall_curve`

    Compute precision-recall pairs for different probability thresholds.

  Examples

      >>> import numpy as np
      >>> from sklearn import metrics
      >>> y = np.array([1, 1, 2, 2])
      >>> pred = np.array([0.1, 0.4, 0.35, 0.8])
      >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
      >>> metrics.auc(fpr, tpr)
      0.75

## Examples using `sklearn.metrics.auc`

Species distribution modeling

Species distribution modeling

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

Precision-Recall

Precision-Recall

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation