Plot Partial Dependence Visualization Api¶
========================================= Advanced Plotting With Partial Dependence¶
The :class:~sklearn.inspection.PartialDependenceDisplay object can be used
for plotting without needing to recalculate the partial dependence. In this
example, we show how to plot partial dependence plots and how to quickly
customize the plot with the visualization API.
… note::
See also :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
Imports for Advanced Partial Dependence Plot Customization¶
PartialDependenceDisplay objects store precomputed partial dependence values and can be re-plotted on different axes without recomputation, enabling flexible multi-model comparisons: Partial dependence plots show the marginal effect of one or two features on a model’s predictions, computed by averaging predictions over all possible values of the other features. The from_estimator class method computes and plots in one step, returning a display object that caches the results in attributes like pd_results and axes_. Subsequent calls to .plot(ax=...) reuse these cached values, which is especially valuable when the underlying model (like MLPRegressor) is expensive to query for the hundreds of grid points needed to draw smooth curves.
Comparing partial dependence curves across models reveals how different architectures capture feature-target relationships: A DecisionTreeRegressor produces step-function partial dependence curves (reflecting its piecewise-constant predictions), while an MLPRegressor produces smooth curves (reflecting its continuous activation functions). By plotting both on shared axes – either stacked in rows or overlaid on the same subplot – you can quickly identify where the models agree (both capture the same feature effect) and where they diverge (one captures a non-linearity the other misses). The line_kw parameter passes keyword arguments like color and label to matplotlib’s plot function, enabling visual differentiation when multiple curves share the same axes.
# noqa: E501
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.inspection import PartialDependenceDisplay
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
# %%
# Train models on the diabetes dataset
# ================================================
#
# First, we train a decision tree and a multi-layer perceptron on the diabetes
# dataset.
diabetes = load_diabetes()
X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
y = diabetes.target
tree = DecisionTreeRegressor()
mlp = make_pipeline(
StandardScaler(),
MLPRegressor(hidden_layer_sizes=(100, 100), tol=1e-2, max_iter=500, random_state=0),
)
tree.fit(X, y)
mlp.fit(X, y)
# %%
# Plotting partial dependence for two features
# ============================================
#
# We plot partial dependence curves for features "age" and "bmi" (body mass
# index) for the decision tree. With two features,
# :func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` expects to plot
# two curves. Here the plot function place a grid of two plots using the space
# defined by `ax` .
fig, ax = plt.subplots(figsize=(12, 6))
ax.set_title("Decision Tree")
tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age", "bmi"], ax=ax)
# %%
# The partial dependence curves can be plotted for the multi-layer perceptron.
# In this case, `line_kw` is passed to
# :func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` to change the
# color of the curve.
fig, ax = plt.subplots(figsize=(12, 6))
ax.set_title("Multi-layer Perceptron")
mlp_disp = PartialDependenceDisplay.from_estimator(
mlp, X, ["age", "bmi"], ax=ax, line_kw={"color": "red"}
)
# %%
# Plotting partial dependence of the two models together
# ======================================================
#
# The `tree_disp` and `mlp_disp`
# :class:`~sklearn.inspection.PartialDependenceDisplay` objects contain all the
# computed information needed to recreate the partial dependence curves. This
# means we can easily create additional plots without needing to recompute the
# curves.
#
# One way to plot the curves is to place them in the same figure, with the
# curves of each model on each row. First, we create a figure with two axes
# within two rows and one column. The two axes are passed to the
# :func:`~sklearn.inspection.PartialDependenceDisplay.plot` functions of
# `tree_disp` and `mlp_disp`. The given axes will be used by the plotting
# function to draw the partial dependence. The resulting plot places the
# decision tree partial dependence curves in the first row of the
# multi-layer perceptron in the second row.
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))
tree_disp.plot(ax=ax1)
ax1.set_title("Decision Tree")
mlp_disp.plot(ax=ax2, line_kw={"color": "red"})
ax2.set_title("Multi-layer Perceptron")
# %%
# Another way to compare the curves is to plot them on top of each other. Here,
# we create a figure with one row and two columns. The axes are passed into the
# :func:`~sklearn.inspection.PartialDependenceDisplay.plot` function as a list,
# which will plot the partial dependence curves of each model on the same axes.
# The length of the axes list must be equal to the number of plots drawn.
# sphinx_gallery_thumbnail_number = 4
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
tree_disp.plot(ax=[ax1, ax2], line_kw={"label": "Decision Tree"})
mlp_disp.plot(
ax=[ax1, ax2], line_kw={"label": "Multi-layer Perceptron", "color": "red"}
)
ax1.legend()
ax2.legend()
# %%
# `tree_disp.axes_` is a numpy array container the axes used to draw the
# partial dependence plots. This can be passed to `mlp_disp` to have the same
# affect of drawing the plots on top of each other. Furthermore, the
# `mlp_disp.figure_` stores the figure, which allows for resizing the figure
# after calling `plot`. In this case `tree_disp.axes_` has two dimensions, thus
# `plot` will only show the y label and y ticks on the left most plot.
tree_disp.plot(line_kw={"label": "Decision Tree"})
mlp_disp.plot(
line_kw={"label": "Multi-layer Perceptron", "color": "red"}, ax=tree_disp.axes_
)
tree_disp.figure_.set_size_inches(10, 6)
tree_disp.axes_[0, 0].legend()
tree_disp.axes_[0, 1].legend()
plt.show()
# %%
# Plotting partial dependence for one feature
# ===========================================
#
# Here, we plot the partial dependence curves for a single feature, "age", on
# the same axes. In this case, `tree_disp.axes_` is passed into the second
# plot function.
tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age"])
mlp_disp = PartialDependenceDisplay.from_estimator(
mlp, X, ["age"], ax=tree_disp.axes_, line_kw={"color": "red"}
)