Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://scikit-learn.org/dev/developers/../modules/generated/sklearn.metrics.auc.html below:

auc — scikit-learn 1.8.dev0 documentation

sklearn.metrics.auc(x, y)[source]#

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:

xarray-like of shape (n,)

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

yarray-like of shape (n,)

Y coordinates.

Returns:

aucfloat

Area Under the Curve.

Examples

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

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4