Pushing the docs to dev/ for branch: main, commit 955ec74c3a40e838a6262f3c17e64024651a1ab3

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -63,6 +63,21 @@
 # means that the final logistic regression step contains the feature names of the input.
 clf[-1].feature_names_in_
 
+# %%
+# .. note:: If one uses the method `set_params`, the transformer will be
+#    replaced by a new one with the default output format.
+clf.set_params(standardscaler=StandardScaler())
+clf.fit(X_train, y_train)
+clf[-1].feature_names_in_
+
+# %%
+# To keep the intended behavior, use `set_output` on the new transformer
+# beforehand
+scaler = StandardScaler().set_output(transform="pandas")
+clf.set_params(standardscaler=scaler)
+clf.fit(X_train, y_train)
+clf[-1].feature_names_in_
+
 # %%
 # Next we load the titanic dataset to demonstrate `set_output` with
 # :class:`compose.ColumnTransformer` and heterogeneous data.

dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py

@@ -97,6 +97,42 @@
         "clf[-1].feature_names_in_"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>If one uses the method `set_params`, the transformer will be\n   replaced by a new one with the default output format.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "clf.set_params(standardscaler=StandardScaler())\nclf.fit(X_train, y_train)\nclf[-1].feature_names_in_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To keep the intended behavior, use `set_output` on the new transformer\nbeforehand\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "scaler = StandardScaler().set_output(transform=\"pandas\")\nclf.set_params(standardscaler=scaler)\nclf.fit(X_train, y_train)\nclf[-1].feature_names_in_"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},