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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=1, verbose=0, ax=None, line_kw=None, contour_kw=None, **fig_kw)[source]

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.


gbrt : BaseGradientBoosting

A fitted gradient boosting model.

X : array-like, shape=(n_samples, n_features)

The data on which gbrt was trained.

features : seq of tuples or ints

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.

feature_names : seq of str

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

label : object

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

n_cols : int

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

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

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

grid_resolution : int, default=100

The number of equally spaced points on the axes.

n_jobs : int

The number of CPUs to use to compute the PDs. -1 means ‘all CPUs’. Defaults to 1.

verbose : int

Verbose output during PD computations. Defaults to 0.

ax : Matplotlib axis object, default None

An axis object onto which the plots will be drawn.

line_kw : dict

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

contour_kw : dict

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

fig_kw : dict

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


fig : figure

The Matplotlib Figure object.

axs : seq of Axis objects

A seq of Axis objects, one for each subplot.


>>> 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)]) 

Examples using sklearn.ensemble.partial_dependence.plot_partial_dependence