From c2126c11cb004a330914307fa250713e606e6e57 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 30 Mar 2023 18:22:58 +0200 Subject: [PATCH] EXA Fix rst in plot_partial_dependence --- examples/inspection/plot_partial_dependence.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py index f2f6c2949bb84..7572f146d6363 100644 --- a/examples/inspection/plot_partial_dependence.py +++ b/examples/inspection/plot_partial_dependence.py @@ -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. @@ -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 @@ -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 @@ -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 @@ -521,7 +521,7 @@ # %% # 3D representation -# """"""""""""""""" +# ~~~~~~~~~~~~~~~~~ # # Let's make the same partial dependence plot for the 2 features interaction, # this time in 3 dimensions.