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 withn_cols
columns. Twoway partial dependence plots are plotted as contour plots. The deciles of the feature values will be shown with tick marks on the xaxes for oneway plots, and on both axes for twoway 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
andGradientBoostingRegressor
, the ‘recursion’ method (used by default) will not account for theinit
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 thatinit
is a constant estimator (which is the default). However, ifinit
is not a constant estimator, the partial dependence values are incorrect for ‘recursion’ because the offset will be sampledependent. It is preferable to use the ‘brute’ method. Note that this only applies toGradientBoostingClassifier
andGradientBoostingRegressor
, not toHistGradientBoostingClassifier
andHistGradientBoostingRegressor
. Parameters
 estimatorBaseEstimator
A fitted estimator object implementing predict, predict_proba, or decision_function. Multioutputmulticlass classifiers are not supported.
 X{arraylike or dataframe} of shape (n_samples, n_features)
X
is used to generate a grid of values for the targetfeatures
(where the partial dependence will be evaluated), and also to generate values for the complement features when themethod
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 oneway PDP is created; if features[i] is a tuple, a twoway PDP is created. Each tuple must be of size 2. if any entry is a string, then it must be in
feature_names
. feature_namesarraylike 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 orNone
. 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:
‘recursion’ is only supported for some treebased estimators (namely
GradientBoostingClassifier
,GradientBoostingRegressor
,HistGradientBoostingClassifier
,HistGradientBoostingRegressor
) but is more efficient in terms of speed. With this method, the target response of a classifier is always the decision function, not the predicted probabilities.‘brute’ is supported for any estimator, but is more computationally intensive.
‘auto’: the ‘recursion’ is used for estimators that support it, and ‘brute’ is used otherwise.
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 ajoblib.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 oneway partial dependence plots. contour_kwdict, optional
Dict with keywords passed to the
matplotlib.pyplot.contourf
call. For twoway partial dependence plots. axMatplotlib axes or arraylike 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 arraylike 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.
 If
New in version 0.22.
 Returns
 display:
PartialDependenceDisplay
 display:
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