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 from_estimator 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.

Results of partial_dependence for features .

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 names corresponding to the indices in features .

• 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.

deciles dict

Deciles for feature indices in features .

kind {‘average’, ‘individual’, ‘both’} or list of such str, 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;

kind='both' results in plotting both the ICE and PD on the same plot.

A list of such strings can be provided to specify kind on a per-plot basis. The length of the list should be the same as the number of interaction requested in features .

ICE (‘individual’ or ‘both’) is not a valid option for 2-ways interactions plot. As a result, an error will be raised. 2-ways interaction plots should always be configured to use the ‘average’ kind instead.

The fast method='recursion' option is only available for kind='average' and sample_weights=None . Computing individual dependencies and doing weighted averages requires using the slower method='brute' .

New in version 0.24: Add kind parameter with 'average' , 'individual' , and 'both' options.

New in version 1.1: Add the possibility to pass a list of string specifying kind for each plot.

subsample float, 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_state int, 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.

is_categorical list of (bool,) or list of (bool, bool), default=None

Whether each target feature in features is categorical or not. The list should be same size as features . If None , all features are assumed to be continuous.

New in version 1.2.

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.

bars_ ndarray of matplotlib Artists

If ax is an axes or None, bars_[i, j] is the partial dependence bar plot on the i-th row and j-th column (for a categorical feature). If ax is a list of axes, bars_[i] is the partial dependence bar 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 bar plot.

New in version 1.2.

heatmaps_ ndarray of matplotlib Artists

If ax is an axes or None, heatmaps_[i, j] is the partial dependence heatmap on the i-th row and j-th column (for a pair of categorical features) . If ax is a list of axes, heatmaps_[i] is the partial dependence heatmap 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 heatmap.

New in version 1.2.

figure_ matplotlib Figure

Figure containing partial dependence plots.

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> from sklearn.inspection import PartialDependenceDisplay
>>> from sklearn.inspection import partial_dependence
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> features, feature_names = [(0,)], [f"Features #{i}" for i in range(X.shape[1])]
>>> deciles = {0: np.linspace(0, 1, num=5)}
>>> pd_results = partial_dependence(
...     clf, X, features=0, kind="average", grid_resolution=5)
>>> display = PartialDependenceDisplay(
...     [pd_results], features=features, feature_names=feature_names,
...     target_idx=0, deciles=deciles
... )
>>> display.plot(pdp_lim={1: (-1.38, 0.66)})
>>> plt.show()
from_estimator(estimator, X, features, *[, ...])
Partial dependence (PD) and individual conditional expectation (ICE) plots.
plot(*[, ax, n_cols, line_kw, ice_lines_kw, ...])
Plot partial dependence plots.
classmethod from_estimator(estimator, X, features, *, sample_weight=None, categorical_features=None, 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, line_kw=None, ice_lines_kw=None, pd_line_kw=None, contour_kw=None, ax=None, kind='average', centered=False, subsample=1000, random_state=None)[source]¶
Partial dependence (PD) and individual conditional expectation (ICE) plots.




    

Partial dependence plots, individual conditional expectation plots or an
overlay of both of them can be plotted by setting the kind
parameter. The len(features) plots are arranged in a grid with
n_cols columns. Two-way partial dependence plots are plotted as
contour plots. The deciles of the feature values will be shown with tick
marks on the x-axes for one-way plots, and on both axes for two-way
plots.
Read more in the User Guide.
PartialDependenceDisplay.from_estimator does not support using the
same axes with multiple calls. To plot the partial dependence for
multiple estimators, please pass the axes created by the first call to the
second call:
>>> from sklearn.inspection import PartialDependenceDisplay
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.ensemble import RandomForestRegressor
>>> X, y = make_friedman1()
>>> est1 = LinearRegression().fit(X, y)
>>> est2 = RandomForestRegressor().fit(X, y)
>>> disp1 = PartialDependenceDisplay.from_estimator(est1, X,
...                                                 [1, 2])
>>> disp2 = PartialDependenceDisplay.from_estimator(est2, X, [1, 2],
...                                                 ax=disp1.axes_)
For GradientBoostingClassifier and
GradientBoostingRegressor, the
'recursion' method (used by default) will not account for the init
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 that init is a constant estimator (which is the
default). However, if init is not a constant estimator, the
partial dependence values are incorrect for 'recursion' because the
offset will be sample-dependent. It is preferable to use the 'brute'
method. Note that this only applies to
GradientBoostingClassifier and
GradientBoostingRegressor, not to
HistGradientBoostingClassifier and
HistGradientBoostingRegressor.
New in version 1.0.
Parameters:
estimatorBaseEstimator
A fitted estimator object implementing predict,
predict_proba, or decision_function.
Multioutput-multiclass classifiers are not supported.
X{array-like, dataframe} of shape (n_samples, n_features)
X is used to generate a grid of values for the target
features (where the partial dependence will be evaluated), and
also to generate values for the complement features when the
method 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 integer or a string, a one-way PDP is created;
if features[i] is a tuple, a two-way PDP is created (only supported
with kind='average'). Each tuple must be of size 2.
If any entry is a string, then it must be in feature_names.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights are used to calculate weighted means when averaging the
model output. If None, then samples are equally weighted. If
sample_weight is not None, then method will be set to 'brute'.
Note that sample_weight is ignored for kind='individual'.
New in version 1.3.
categorical_featuresarray-like of shape (n_features,) or shape                 (n_categorical_features,), dtype={bool, int, str}, default=None
Indicates the categorical features.
None: no feature will be considered categorical;
boolean array-like: boolean mask of shape (n_features,)
indicating which features are categorical. Thus, this array has
the same shape has X.shape[1];
integer or string array-like: integer indices or strings
indicating categorical features.
New in version 1.2.
feature_namesarray-like 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, 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’, ‘decision_function’},                 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, default=3
The maximum number of columns in the grid plot. Only active when ax
is a single axis or None.
grid_resolutionint, default=100
The number of equally spaced points on the axes of the plots, for each
target feature.
percentilestuple of float, 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, default=’auto’
The method used to calculate the averaged predictions:
'recursion' is only supported for some tree-based estimators
(namely
GradientBoostingClassifier,
GradientBoostingRegressor,
HistGradientBoostingClassifier,
HistGradientBoostingRegressor,
DecisionTreeRegressor,
RandomForestRegressor
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. Since the 'recursion' method implicitly computes
the average of the ICEs by design, it is not compatible with ICE and
thus kind must be 'average'.
'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. If sample_weight is not None,
then 'brute' is used regardless of the estimator.
Please see this note for
differences between the 'brute' and 'recursion' method.
n_jobsint, default=None
The number of CPUs to use to compute the partial dependences.
Computation is parallelized over features specified by the features
parameter.
None means 1 unless in a joblib.parallel_backend context.
-1 means using all processors. See Glossary
for more details.
verboseint, default=0
Verbose output during PD computations.
line_kwdict, default=None
Dict with keywords passed to the matplotlib.pyplot.plot call.
For one-way partial dependence plots. It can be used to define common
properties for both ice_lines_kw and pdp_line_kw.
ice_lines_kwdict, default=None
Dictionary with keywords passed to the matplotlib.pyplot.plot call.
For ICE lines in the one-way partial dependence plots.
The key value pairs defined in ice_lines_kw takes priority over
line_kw.
pd_line_kwdict, default=None
Dictionary with keywords passed to the matplotlib.pyplot.plot call.
For partial dependence in one-way partial dependence plots.
The key value pairs defined in pd_line_kw takes priority over
line_kw.
contour_kwdict, default=None
Dict with keywords passed to the matplotlib.pyplot.contourf call.
For two-way partial dependence plots.
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.
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' and sample_weights=None. Computing individual
dependencies and doing weighted averages requires using the slower
method='brute'.
centeredbool, default=False
If True, the ICE and PD lines will start at the origin of the
y-axis. By default, no centering is done.
New in version 1.1.
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
absolute number samples to use.
Note that the full dataset is still used to calculate averaged partial
dependence when kind='both'.
random_stateint, RandomState instance or None, default=None
Controls the randomness of the selected samples when subsamples is not
None and kind is either 'both' or 'individual'.
See Glossary for details.
Returns:
displayPartialDependenceDisplay

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> from sklearn.inspection import PartialDependenceDisplay
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> PartialDependenceDisplay.from_estimator(clf, X, [0, (0, 1)])
>>> plt.show()
plot(*, ax=None, n_cols=3, line_kw=None, ice_lines_kw=None, pd_line_kw=None, contour_kw=None, bar_kw=None, heatmap_kw=None, pdp_lim=None, centered=False)[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.
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.
ice_lines_kwdict, default=None
Dictionary with keywords passed to the matplotlib.pyplot.plot call.
For ICE lines in the one-way partial dependence plots.
The key value pairs defined in ice_lines_kw takes priority over
line_kw.
New in version 1.0.
pd_line_kwdict, default=None
Dictionary with keywords passed to the matplotlib.pyplot.plot call.
For partial dependence in one-way partial dependence plots.
The key value pairs defined in pd_line_kw takes priority over
line_kw.
New in version 1.0.
contour_kwdict, default=None
Dict with keywords passed to the matplotlib.pyplot.contourf
call for two-way partial dependence plots.
bar_kwdict, default=None
Dict with keywords passed to the matplotlib.pyplot.bar
call for one-way categorical partial dependence plots.
New in version 1.2.
heatmap_kwdict, default=None
Dict with keywords passed to the matplotlib.pyplot.imshow
call for two-way categorical partial dependence plots.
New in version 1.2.
pdp_limdict, default=None
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. If None (default), the limit will be
inferred from the global minimum and maximum of all predictions.
New in version 1.1.
centeredbool, default=False
If True, the ICE and PD lines will start at the origin of the
y-axis. By default, no centering is done.
New in version 1.1.
Examples using sklearn.inspection.PartialDependenceDisplay¶
Advanced Plotting With Partial Dependence
  
Advanced Plotting With Partial Dependence
Examples using sklearn.inspection.PartialDependenceDisplay.from_estimator¶
Release Highlights for scikit-learn 1.4
  
Release Highlights for scikit-learn 1.4
Release Highlights for scikit-learn 1.2
  
Release Highlights for scikit-learn 1.2
Release Highlights for scikit-learn 0.24
  
Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.23
  
Release Highlights for scikit-learn 0.23
Monotonic Constraints
  
Monotonic Constraints
Partial Dependence and Individual Conditional Expectation Plots
  
Partial Dependence and Individual Conditional Expectation Plots
Advanced Plotting With Partial Dependence
  
Advanced Plotting With Partial Dependence