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EXA Fix rst in plot_partial_dependence #26028

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Mar 30, 2023
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EXA Fix rst in plot_partial_dependence #26028

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10 changes: 5 additions & 5 deletions examples/inspection/plot_partial_dependence.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,7 +198,7 @@
# features and individual conditional expectation (ICE).
#
# Multi-layer perceptron
# """"""""""""""""""""""
# ~~~~~~~~~~~~~~~~~~~~~~
#
# Let's fit a :class:`~sklearn.neural_network.MLPRegressor` and compute
# single-variable partial dependence plots.
Expand Down Expand Up @@ -278,7 +278,7 @@

# %%
# Gradient boosting
# """""""""""""""""
# ~~~~~~~~~~~~~~~~~
#
# Let's now fit a :class:`~sklearn.ensemble.HistGradientBoostingRegressor` and
# compute the partial dependence on the same features. We also use the
Expand Down Expand Up @@ -330,7 +330,7 @@

# %%
# Analysis of the plots
# """""""""""""""""""""
# ~~~~~~~~~~~~~~~~~~~~~
#
# We will first look at the PDPs for the numerical features. For both models, the
# general trend of the PDP of the temperature is that the number of bike rentals is
Expand All @@ -352,7 +352,7 @@
# synthetic samples if features are correlated.
#
# ICE vs. PDP
# """""""""""
# ~~~~~~~~~~~
# PDP is an average of the marginal effects of the features. We are averaging the
# response of all samples of the provided set. Thus, some effects could be hidden. In
# this regard, it is possible to plot each individual response. This representation is
Expand Down Expand Up @@ -521,7 +521,7 @@

# %%
# 3D representation
# """""""""""""""""
# ~~~~~~~~~~~~~~~~~
#
# Let's make the same partial dependence plot for the 2 features interaction,
# this time in 3 dimensions.
Expand Down