sklearn.ensemble.partial_dependence.plot_partial_dependence

sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None, n_cols=3, grid_resolution=100, percentiles=(0.05, 0.95), n_jobs=None, verbose=0, ax=None, line_kw=None, contour_kw=None, **fig_kw)[source]

DEPRECATED: The function ensemble.plot_partial_dependence has been deprecated in favour of sklearn.inspection.plot_partial_dependence in 0.21 and will be removed in 0.23.

Partial dependence plots for features.

The len(features) plots are arranged in a grid with n_cols columns. Two-way partial dependence plots are plotted as contour plots.

Read more in the User Guide.

Deprecated since version 0.21: This function was deprecated in version 0.21 in favor of sklearn.inspection.plot_partial_dependence and will be removed in 0.23.

Parameters
gbrtBaseGradientBoosting

A fitted gradient boosting model.

Xarray-like of shape (n_samples, n_features)

The data on which gbrt was trained.

featuresseq of ints, strings, or tuples of ints or strings

If seq[i] is an int or a tuple with one int value, a one-way PDP is created; if seq[i] is a tuple of two ints, a two-way PDP is created. If feature_names is specified and seq[i] is an int, seq[i] must be < len(feature_names). If seq[i] is a string, feature_names must be specified, and seq[i] must be in feature_names.

feature_namesseq of str

Name of each feature; feature_names[i] holds the name of the feature with index i.

labelobject

The class label for which the PDPs should be computed. Only if gbrt is a multi-class model. Must be in gbrt.classes_.

n_colsint

The number of columns in the grid plot (default: 3).

grid_resolutionint, default=100

The number of equally spaced points on the axes.

percentiles(low, high), default=(0.05, 0.95)

The lower and upper percentile used to create the extreme values for the PDP axes.

n_jobsint or None, optional (default=None)

None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verboseint

Verbose output during PD computations. Defaults to 0.

axMatplotlib axis object, default None

An axis object onto which the plots will be drawn.

line_kwdict

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

contour_kwdict

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

**fig_kwdict

Dict with keywords passed to the figure() call. Note that all keywords not recognized above will be automatically included here.

Returns
figfigure

The Matplotlib Figure object.

axsseq of Axis objects

A seq of Axis objects, one for each subplot.

Examples

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