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Oct 21, 2021
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DOC highlight source of biases feature importances #21384

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6 changes: 3 additions & 3 deletions examples/ensemble/plot_forest_importances.py
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Expand Up @@ -52,9 +52,9 @@
# deviation of accumulation of the impurity decrease within each tree.
#
# .. warning::
# Impurity-based feature importances can be misleading for high cardinality
# features (many unique values). See :ref:`permutation_importance` as
# an alternative below.
# Impurity-based feature importances can be misleading for **high
# cardinality** features (many unique values). See
# :ref:`permutation_importance` as an alternative below.
import time
import numpy as np

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6 changes: 3 additions & 3 deletions examples/ensemble/plot_forest_importances_faces.py
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Expand Up @@ -56,9 +56,9 @@
# deviation of accumulation of the impurity decrease within each tree.
#
# .. warning::
# Impurity-based feature importances can be misleading for high cardinality
# features (many unique values). See :ref:`permutation_importance` as
# an alternative.
# Impurity-based feature importances can be misleading for **high
# cardinality** features (many unique values). See
# :ref:`permutation_importance` as an alternative.
import time
import matplotlib.pyplot as plt

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27 changes: 14 additions & 13 deletions examples/ensemble/plot_gradient_boosting_regression.py
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Expand Up @@ -45,19 +45,19 @@
# for testing. We will also set the regression model parameters. You can play
# with these parameters to see how the results change.
#
# n_estimators : the number of boosting stages that will be performed.
# `n_estimators` : the number of boosting stages that will be performed.
# Later, we will plot deviance against boosting iterations.
#
# max_depth : limits the number of nodes in the tree.
# `max_depth` : limits the number of nodes in the tree.
# The best value depends on the interaction of the input variables.
#
# min_samples_split : the minimum number of samples required to split an
# `min_samples_split` : the minimum number of samples required to split an
# internal node.
#
# learning_rate : how much the contribution of each tree will shrink.
# `learning_rate` : how much the contribution of each tree will shrink.
#
# loss : loss function to optimize. The least squares function is used in this
# case however, there are many other options (see
# `loss` : loss function to optimize. The least squares function is used in
# this case however, there are many other options (see
# :class:`~sklearn.ensemble.GradientBoostingRegressor` ).

X_train, X_test, y_train, y_test = train_test_split(
Expand All @@ -74,7 +74,7 @@

# %%
# Fit regression model
# -------------------------------------
# --------------------
#
# Now we will initiate the gradient boosting regressors and fit it with our
# training data. Let's also look and the mean squared error on the test data.
Expand All @@ -87,7 +87,7 @@

# %%
# Plot training deviance
# -------------------------------------
# ----------------------
#
# Finally, we will visualize the results. To do that we will first compute the
# test set deviance and then plot it against boosting iterations.
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# %%
# Plot feature importance
# -------------------------------------
# -----------------------
#
# Careful, impurity-based feature importances can be misleading for
# high cardinality features (many unique values). As an alternative,
# the permutation importances of ``reg`` can be computed on a
# held out test set. See :ref:`permutation_importance` for more details.
# .. warning::
# Careful, impurity-based feature importances can be misleading for
# **high cardinality** features (many unique values). As an alternative,
# the permutation importances of ``reg`` can be computed on a
# held out test set. See :ref:`permutation_importance` for more details.
#
# For this example, the impurity-based and permutation methods identify the
# same 2 strongly predictive features but not in the same order. The third most
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