[ci skip] Feature branch to update to 1.6 (#813)

* Feature branch to update to 1.6

* MNT Fix several FutureWarnings (#810)

* MTN Wrap up quiz sklearn 1.6 verification (#817)

* MAINT Use class_of_interest in DecisionBoundaryDisplay (#772)

* Resync everything

---------

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
Co-authored-by: SebastienMelo <125986598+SebastienMelo@users.noreply.github.com> c9728c8

Author: ogrisel

_images/8093d6c62d5e837118e0dc5cc52e7adbe982e875adb370ab83d7232e06d26691.png

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_images/a94f76db6c61d6fec689c0c47babccf1982ef9c2bb0795c1961923dedbf9721e.png

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_sources/python_scripts/01_tabular_data_exploration_ex_01.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_sources/python_scripts/02_numerical_pipeline_ex_00.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_sources/python_scripts/02_numerical_pipeline_ex_01.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3
@@ -112,26 +112,7 @@
 # Write your code here.
 
 # %% [markdown]
-# ### Analysis
-#
-# From an accuracy point of view, the result is almost exactly the same. The
-# reason is that `HistGradientBoostingClassifier` is expressive and robust
-# enough to deal with misleading ordering of integer coded categories (which was
-# not the case for linear models).
-#
-# However from a computation point of view, the training time is much longer:
-# this is caused by the fact that `OneHotEncoder` generates more features than
-# `OrdinalEncoder`; for each unique categorical value a column is created.
-#
-# Note that the current implementation `HistGradientBoostingClassifier` is still
-# incomplete, and once sparse representation are handled correctly, training
-# time might improve with such kinds of encodings.
-#
-# The main take away message is that arbitrary integer coding of categories is
-# perfectly fine for `HistGradientBoostingClassifier` and yields fast training
-# times.
-
-# Which encoder should I use?
+# ## Which encoder should I use?
 #
 # |                  | Meaningful order              | Non-meaningful order |
 # | ---------------- | ----------------------------- | -------------------- |

_sources/python_scripts/03_categorical_pipeline_ex_01.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_sources/python_scripts/03_categorical_pipeline_ex_02.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.7
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3
 #     name: python3

_sources/python_scripts/cross_validation_ex_01.py

@@ -155,7 +155,7 @@
 # smoother visualization.
 
 # %%
-data_ride.resample("60S").mean().plot()
+data_ride.resample("60s").mean().plot()
 plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
 _ = plt.title("Sensor values for different cyclist measurements")