sklearn.inspection.plot_partial_dependence¶

sklearn.inspection.plot_partial_dependence(estimator, X, features, feature_names=None, target=None, response_method='auto', n_cols=3, grid_resolution=100, percentiles=(0.05, 0.95), method='auto', n_jobs=None, verbose=0, fig=None, line_kw=None, contour_kw=None, ax=None)[source]

Partial dependence plots.

The len(features) plots are arranged in a grid with n_cols columns. Two-way partial dependence plots are plotted as contour plots. The deciles of the feature values will be shown with tick marks on the x-axes for one-way plots, and on both axes for two-way plots.

Read more in the User Guide.

Note

plot_partial_dependence does not support using the same axes with multiple calls. To plot the the partial dependence for multiple estimators, please pass the axes created by the first call to the second call:

>>> from sklearn.inspection import plot_partial_dependence
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.linear_model import LinearRegression
>>> X, y = make_friedman1()
>>> est = LinearRegression().fit(X, y)
>>> disp1 = plot_partial_dependence(est, X)
>>> disp2 = plot_partial_dependence(est, X,
...                                 ax=disp1.axes_)


Warning

For GradientBoostingClassifier and GradientBoostingRegressor, the ‘recursion’ method (used by default) will not account for the init predictor of the boosting process. In practice, this will produce the same values as ‘brute’ up to a constant offset in the target response, provided that init is a constant estimator (which is the default). However, if init is not a constant estimator, the partial dependence values are incorrect for ‘recursion’ because the offset will be sample-dependent. It is preferable to use the ‘brute’ method. Note that this only applies to GradientBoostingClassifier and GradientBoostingRegressor, not to HistGradientBoostingClassifier and HistGradientBoostingRegressor.

Parameters
estimatorBaseEstimator

A fitted estimator object implementing predict, predict_proba, or decision_function. Multioutput-multiclass classifiers are not supported.

X{array-like or dataframe} of shape (n_samples, n_features)

X is used to generate a grid of values for the target features (where the partial dependence will be evaluated), and also to generate values for the complement features when the method is ‘brute’.

featureslist of {int, str, pair of int, pair of str}

The target features for which to create the PDPs. If features[i] is an int or a string, a one-way PDP is created; if features[i] is a tuple, a two-way PDP is created. Each tuple must be of size 2. if any entry is a string, then it must be in feature_names.

feature_namesarray-like of shape (n_features,), dtype=str, default=None

Name of each feature; feature_names[i] holds the name of the feature with index i. By default, the name of the feature corresponds to their numerical index for NumPy array and their column name for pandas dataframe.

targetint, optional (default=None)
• In a multiclass setting, specifies the class for which the PDPs should be computed. Note that for binary classification, the positive class (index 1) is always used.

• In a multioutput setting, specifies the task for which the PDPs should be computed.

Ignored in binary classification or classical regression settings.

response_method‘auto’, ‘predict_proba’ or ‘decision_function’, optional (default=’auto’)

Specifies whether to use predict_proba or decision_function as the target response. For regressors this parameter is ignored and the response is always the output of predict. By default, predict_proba is tried first and we revert to decision_function if it doesn’t exist. If method is ‘recursion’, the response is always the output of decision_function.

n_colsint, optional (default=3)

The maximum number of columns in the grid plot. Only active when ax is a single axis or None.

grid_resolutionint, optional (default=100)

The number of equally spaced points on the axes of the plots, for each target feature.

percentilestuple of float, optional (default=(0.05, 0.95))

The lower and upper percentile used to create the extreme values for the PDP axes. Must be in [0, 1].

methodstr, optional (default=’auto’)

The method used to calculate the averaged predictions:

Please see this note for differences between the ‘brute’ and ‘recursion’ method.

n_jobsint, optional (default=None)

The number of CPUs to use to compute the partial dependences. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verboseint, optional (default=0)

Verbose output during PD computations.

figMatplotlib figure object, optional (default=None)

A figure object onto which the plots will be drawn, after the figure has been cleared. By default, a new one is created.

Deprecated since version 0.22: fig will be removed in 0.24.

line_kwdict, optional

Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots.

contour_kwdict, optional

Dict with keywords passed to the matplotlib.pyplot.contourf call. For two-way partial dependence plots.

axMatplotlib axes or array-like of Matplotlib axes, default=None
• If a single axis is passed in, it is treated as a bounding axes

and a grid of partial dependence plots will be drawn within these bounds. The n_cols parameter controls the number of columns in the grid.

• If an array-like of axes are passed in, the partial dependence

plots will be drawn directly into these axes.

• If None, a figure and a bounding axes is created and treated

as the single axes case.

New in version 0.22.

Returns
display: PartialDependenceDisplay

See also

sklearn.inspection.partial_dependence

Return raw partial dependence values

Examples

>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> plot_partial_dependence(clf, X, [0, (0, 1)]) #doctest: +SKIP