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 withn_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, 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 ajoblib.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_kw
dictDict 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)]) ...