sklearn.inspection.PartialDependenceDisplay

class sklearn.inspection.PartialDependenceDisplay(pd_results, *, features, feature_names, target_idx, pdp_lim, deciles, kind='average', subsample=1000, random_state=None)[source]

Partial Dependence Plot (PDP).

This can also display individual partial dependencies which are often referred to as: Individual Condition Expectation (ICE).

It is recommended to use plot_partial_dependence to create a PartialDependenceDisplay. All parameters are stored as attributes.

Read more in Advanced Plotting With Partial Dependence and the User Guide.

New in version 0.22.

Parameters
pd_resultslist of Bunch

Results of partial_dependence for features.

featureslist of (int,) or list of (int, int)

Indices of features for a given plot. A tuple of one integer will plot a partial dependence curve of one feature. A tuple of two integers will plot a two-way partial dependence curve as a contour plot.

feature_nameslist of str

Feature names corresponding to the indices in features.

target_idxint

  • 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.

pdp_limdict

Global min and max average predictions, such that all plots will have the same scale and y limits. pdp_lim[1] is the global min and max for single partial dependence curves. pdp_lim[2] is the global min and max for two-way partial dependence curves.

decilesdict

Deciles for feature indices in features.

kind{‘average’, ‘individual’, ‘both’}, default=’average’

Whether to plot the partial dependence averaged across all the samples in the dataset or one line per sample or both.

  • kind='average' results in the traditional PD plot;

  • kind='individual' results in the ICE plot.

Note that the fast method='recursion' option is only available for kind='average'. Plotting individual dependencies requires using the slower method='brute' option.

New in version 0.24.

subsamplefloat, int or None, default=1000

Sampling for ICE curves when kind is ‘individual’ or ‘both’. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to be used to plot ICE curves. If int, represents the maximum absolute number of samples to use.

Note that the full dataset is still used to calculate partial dependence when kind='both'.

New in version 0.24.

random_stateint, RandomState instance or None, default=None

Controls the randomness of the selected samples when subsamples is not None. See Glossary for details.

New in version 0.24.

Attributes
bounding_ax_matplotlib Axes or None

If ax is an axes or None, the bounding_ax_ is the axes where the grid of partial dependence plots are drawn. If ax is a list of axes or a numpy array of axes, bounding_ax_ is None.

axes_ndarray of matplotlib Axes

If ax is an axes or None, axes_[i, j] is the axes on the i-th row and j-th column. If ax is a list of axes, axes_[i] is the i-th item in ax. Elements that are None correspond to a nonexisting axes in that position.

lines_ndarray of matplotlib Artists

If ax is an axes or None, lines_[i, j] is the partial dependence curve on the i-th row and j-th column. If ax is a list of axes, lines_[i] is the partial dependence curve corresponding to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a line plot.

deciles_vlines_ndarray of matplotlib LineCollection

If ax is an axes or None, vlines_[i, j] is the line collection representing the x axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_[i] corresponds to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a PDP plot.

New in version 0.23.

deciles_hlines_ndarray of matplotlib LineCollection

If ax is an axes or None, vlines_[i, j] is the line collection representing the y axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_[i] corresponds to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a 2-way plot.

New in version 0.23.

contours_ndarray of matplotlib Artists

If ax is an axes or None, contours_[i, j] is the partial dependence plot on the i-th row and j-th column. If ax is a list of axes, contours_[i] is the partial dependence plot corresponding to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a contour plot.

figure_matplotlib Figure

Figure containing partial dependence plots.

See also

partial_dependence

Compute Partial Dependence values.

plot_partial_dependence

Plot Partial Dependence.

Methods

plot(*[, ax, n_cols, line_kw, contour_kw])

Plot partial dependence plots.
plot(*, ax=None, n_cols=3, line_kw=None, contour_kw=None)[source]

Plot partial dependence plots.

Parameters
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.

n_colsint, default=3

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

line_kwdict, default=None

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

contour_kwdict, default=None

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

Returns
displayPartialDependenceDisplay

Examples using sklearn.inspection.PartialDependenceDisplay

Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence