Pushing the docs to dev/ for branch: main, commit 52ba1d00bb63a7405c7fc22e991234657502aeae

Author: dirty-cat-ci

dev/_downloads/0da1186528765de7400f245b4d95af54/10_expressions_intro.zip

@@ -97,7 +97,7 @@
 #   ``X.skb.apply(skrub.optional(StandardScaler()))``
 #
 # Choices can be given a name which is used to display hyperparameter search
-# results and plots or to override their outcome.
+# results and plots or to override their outcome. The name is optional.
 #
 # Note that ``skrub.choose_float()`` and ``skrub.choose_int()`` can be given a
 # ``log`` argument to sample in log scale.

dev/_downloads/1beb4a6828e42ba7f09ae925e26e794b/02_text_with_string_encoders.zip

@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "For the sake of the example, we will focus on the number of ``MinHashEncoder``\ncomponents and the ``learning_rate`` of the ``HistGradientBoostingClassifier``\nto illustrate the ``skrub.choose_from(...)`` objects.\nWhen we use a scikit-learn hyperparameter-tuner like ``GridSearchCV`` or\n``RandomizedSearchCV``, we need to specify a grid of hyperparameters separately\nfrom the estimator, with something similar to\n``GridSearchCV(my_pipeline, param_grid={\"encoder__n_components: [5, 10, 20]\"})``.\nInstead, with skrub we can use\n``skrub.choose_from(...)`` directly where the actual value\nwould normally go. Skrub then takes care of constructing the\n``GridSearchCV``'s parameter grid for us.\n\nSeveral utilities are available:\n\n- ``choose_from`` to choose from a discrete set of values\n- ``choose_float`` and ``choose_int`` to sample numbers in a given range\n- ``choose_bool`` to choose between ``True`` and ``False``\n- ``optional`` to choose between something and ``None``; typically to make a\n  transformation step optional such as\n  ``X.skb.apply(skrub.optional(StandardScaler()))``\n\nChoices can be given a name which is used to display hyperparameter search\nresults and plots or to override their outcome.\n\nNote that ``skrub.choose_float()`` and ``skrub.choose_int()`` can be given a\n``log`` argument to sample in log scale.\n\n"
+        "For the sake of the example, we will focus on the number of ``MinHashEncoder``\ncomponents and the ``learning_rate`` of the ``HistGradientBoostingClassifier``\nto illustrate the ``skrub.choose_from(...)`` objects.\nWhen we use a scikit-learn hyperparameter-tuner like ``GridSearchCV`` or\n``RandomizedSearchCV``, we need to specify a grid of hyperparameters separately\nfrom the estimator, with something similar to\n``GridSearchCV(my_pipeline, param_grid={\"encoder__n_components: [5, 10, 20]\"})``.\nInstead, with skrub we can use\n``skrub.choose_from(...)`` directly where the actual value\nwould normally go. Skrub then takes care of constructing the\n``GridSearchCV``'s parameter grid for us.\n\nSeveral utilities are available:\n\n- ``choose_from`` to choose from a discrete set of values\n- ``choose_float`` and ``choose_int`` to sample numbers in a given range\n- ``choose_bool`` to choose between ``True`` and ``False``\n- ``optional`` to choose between something and ``None``; typically to make a\n  transformation step optional such as\n  ``X.skb.apply(skrub.optional(StandardScaler()))``\n\nChoices can be given a name which is used to display hyperparameter search\nresults and plots or to override their outcome. The name is optional.\n\nNote that ``skrub.choose_float()`` and ``skrub.choose_int()`` can be given a\n``log`` argument to sample in log scale.\n\n"
       ]
     },
     {