Pushing the docs to dev/ for branch: main, commit 102663d6147e552a4e04dda66127e82d6f7e6e25

Author: sklearn-ci

Diff for: dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 2478929b8f68b6599631942f204e49da
+config: d926c2f4528fe6bc7527c4e7380cc80a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

Diff for: dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -635,7 +635,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We observe that tuning the decision threshold increases the expected profit\nwhen deploying our model - as indicated by the business metric. It is therefore\nvaluable, whenever possible, to optimize the decision threshold with respect\nto the business metric.\n\n### Manually setting the decision threshold instead of tuning it\n\nIn the previous example, we used the\n:class:`~sklearn.model_selection.TunedThresholdClassifierCV` to find the optimal\ndecision threshold. However, in some cases, we might have some prior knowledge about\nthe problem at hand and we might be happy to set the decision threshold manually.\n\nThe class :class:`~sklearn.model_selection.FixedThresholdClassifier` allows us to\nmanually set the decision threshold. At prediction time, it behave as the previous\ntuned model but no search is performed during the fitting process.\n\nHere, we will reuse the decision threshold found in the previous section to create a\nnew model and check that it gives the same results.\n\n"
+        "We observe that tuning the decision threshold increases the expected profit\nwhen deploying our model - as indicated by the business metric. It is therefore\nvaluable, whenever possible, to optimize the decision threshold with respect\nto the business metric.\n\n### Manually setting the decision threshold instead of tuning it\n\nIn the previous example, we used the\n:class:`~sklearn.model_selection.TunedThresholdClassifierCV` to find the optimal\ndecision threshold. However, in some cases, we might have some prior knowledge about\nthe problem at hand and we might be happy to set the decision threshold manually.\n\nThe class :class:`~sklearn.model_selection.FixedThresholdClassifier` allows us to\nmanually set the decision threshold. At prediction time, it behave as the previous\ntuned model but no search is performed during the fitting process. Note that here\nwe use :class:`~sklearn.frozen.FrozenEstimator` to wrap the predictive model to\navoid any refitting.\n\nHere, we will reuse the decision threshold found in the previous section to create a\nnew model and check that it gives the same results.\n\n"
       ]
     },
     {
@@ -646,7 +646,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import FixedThresholdClassifier\n\nmodel_fixed_threshold = FixedThresholdClassifier(\n    estimator=model, threshold=tuned_model.best_threshold_, prefit=True\n).fit(data_train, target_train)"
+        "from sklearn.frozen import FrozenEstimator\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nmodel_fixed_threshold = FixedThresholdClassifier(\n    estimator=FrozenEstimator(model), threshold=tuned_model.best_threshold_\n)"
       ]
     },
     {

Diff for: dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -660,15 +660,18 @@ def business_metric(y_true, y_pred, amount):
 #
 # The class :class:`~sklearn.model_selection.FixedThresholdClassifier` allows us to
 # manually set the decision threshold. At prediction time, it behave as the previous
-# tuned model but no search is performed during the fitting process.
+# tuned model but no search is performed during the fitting process. Note that here
+# we use :class:`~sklearn.frozen.FrozenEstimator` to wrap the predictive model to
+# avoid any refitting.
 #
 # Here, we will reuse the decision threshold found in the previous section to create a
 # new model and check that it gives the same results.
+from sklearn.frozen import FrozenEstimator
 from sklearn.model_selection import FixedThresholdClassifier
 
 model_fixed_threshold = FixedThresholdClassifier(
-    estimator=model, threshold=tuned_model.best_threshold_, prefit=True
-).fit(data_train, target_train)
+    estimator=FrozenEstimator(model), threshold=tuned_model.best_threshold_
+)
 
 # %%
 business_score = business_scorer(