`sklearn.calibration`.CalibrationDisplay#

class sklearn.calibration.CalibrationDisplay(prob_true, prob_pred, y_prob, *, estimator_name=None, pos_label=None)[source]#

Calibration curve (also known as reliability diagram) visualization.

It is recommended to use from_estimator or from_predictions to create a CalibrationDisplay. All parameters are stored as attributes.

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

New in version 1.0.

Parameters:

prob_truendarray of shape (n_bins,): The proportion of samples whose class is the positive class (fraction of positives), in each bin.
prob_predndarray of shape (n_bins,): The mean predicted probability in each bin.
y_probndarray of shape (n_samples,): Probability estimates for the positive class, for each sample.
estimator_namestr, default=None: Name of estimator. If None, the estimator name is not shown.
pos_labelint, float, bool or str, default=None: The positive class when computing the calibration curve. By default, pos_label is set to estimators.classes_[1] when using from_estimator and set to 1 when using from_predictions.

New in version 1.1.

Attributes:

line_matplotlib Artist: Calibration curve.
ax_matplotlib Axes: Axes with calibration curve.
figure_matplotlib Figure: Figure containing the curve.

See also

calibration_curve: Compute true and predicted probabilities for a calibration curve.
CalibrationDisplay.from_predictions: Plot calibration curve using true and predicted labels.
CalibrationDisplay.from_estimator: Plot calibration curve using an estimator and data.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.calibration import calibration_curve, CalibrationDisplay
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> clf = LogisticRegression(random_state=0)
>>> clf.fit(X_train, y_train)
LogisticRegression(random_state=0)
>>> y_prob = clf.predict_proba(X_test)[:, 1]
>>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10)
>>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob)
>>> disp.plot()
<...>

Methods

`from_estimator`(estimator, X, y, *[, n_bins, ...])	Plot calibration curve using a binary classifier and data.
`from_predictions`(y_true, y_prob, *[, ...])	Plot calibration curve using true labels and predicted probabilities.
`plot`(*[, ax, name, ref_line])	Plot visualization.

classmethod from_estimator(estimator, X, y, *, n_bins=5, strategy='uniform', pos_label=None, name=None, ref_line=True, ax=None, **kwargs)[source]#

Plot calibration curve using a binary classifier and data.

A calibration curve, also known as a reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis.

Extra keyword arguments will be passed to matplotlib.pyplot.plot.

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

New in version 1.0.

Parameters:

estimatorestimator instance

Fitted classifier or a fitted Pipeline in which the last estimator is a classifier. The classifier must have a predict_proba method.

X{array-like, sparse matrix} of shape (n_samples, n_features)

Input values.

yarray-like of shape (n_samples,)

Binary target values.

n_binsint, default=5

Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data.

strategy{‘uniform’, ‘quantile’}, default=’uniform’

Strategy used to define the widths of the bins.

'uniform': The bins have identical widths.
'quantile': The bins have the same number of samples and depend on predicted probabilities.

pos_labelint, float, bool or str, default=None

The positive class when computing the calibration curve. By default, estimators.classes_[1] is considered as the positive class.

New in version 1.1.

namestr, default=None

Name for labeling curve. If None, the name of the estimator is used.

ref_linebool, default=True

If True, plots a reference line representing a perfectly calibrated classifier.

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

**kwargsdict

Keyword arguments to be passed to matplotlib.pyplot.plot.

Returns:

displayCalibrationDisplay.: Object that stores computed values.

See also

CalibrationDisplay.from_predictions: Plot calibration curve using true and predicted labels.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.calibration import CalibrationDisplay
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> clf = LogisticRegression(random_state=0)
>>> clf.fit(X_train, y_train)
LogisticRegression(random_state=0)
>>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
>>> plt.show()

../../_images/sklearn-calibration-CalibrationDisplay-1.png

classmethod from_predictions(y_true, y_prob, *, n_bins=5, strategy='uniform', pos_label=None, name=None, ref_line=True, ax=None, **kwargs)[source]#

Plot calibration curve using true labels and predicted probabilities.

Calibration curve, also known as reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis.

Extra keyword arguments will be passed to matplotlib.pyplot.plot.

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

New in version 1.0.

Parameters:

y_truearray-like of shape (n_samples,)

True labels.

y_probarray-like of shape (n_samples,)

The predicted probabilities of the positive class.

n_binsint, default=5

Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data.

strategy{‘uniform’, ‘quantile’}, default=’uniform’

Strategy used to define the widths of the bins.

'uniform': The bins have identical widths.
'quantile': The bins have the same number of samples and depend on predicted probabilities.

pos_labelint, float, bool or str, default=None

The positive class when computing the calibration curve. By default pos_label is set to 1.

New in version 1.1.

namestr, default=None

Name for labeling curve.

ref_linebool, default=True

If True, plots a reference line representing a perfectly calibrated classifier.

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

**kwargsdict

Keyword arguments to be passed to matplotlib.pyplot.plot.

Returns:

displayCalibrationDisplay.: Object that stores computed values.

Examples using `sklearn.calibration.CalibrationDisplay.from_estimator`#

Comparison of Calibration of Classifiers

Probability Calibration curves

sklearn.calibration.CalibrationDisplay#

Examples using sklearn.calibration.CalibrationDisplay.from_estimator#

`sklearn.calibration`.CalibrationDisplay#

Examples using `sklearn.calibration.CalibrationDisplay.from_estimator`#