docs: add more preprocessing models into the docs menu.

docs/templates/toc.yml

@@ -121,10 +121,18 @@
     - items:
       - name: Overview
         uid: bigframes.ml.preprocessing
-      - name: OneHotEncoder
-        uid: bigframes.ml.preprocessing.OneHotEncoder
+      - name: KBinsDiscretizer
+        uid: bigframes.ml.preprocessing.KBinsDiscretizer
+      - name: LabelEncoder
+        uid: bigframes.ml.preprocessing.LabelEncoder
+      - name: MaxAbsScaler
+        uid: bigframes.ml.preprocessing.MaxAbsScaler
+      - name: MinMaxScaler
+        uid: bigframes.ml.preprocessing.MinMaxScaler
       - name: StandardScaler
         uid: bigframes.ml.preprocessing.StandardScaler
+      - name: OneHotEncoder
+        uid: bigframes.ml.preprocessing.OneHotEncoder
       name: preprocessing
     name: bigframes.ml
   name: BigQuery DataFrames

third_party/bigframes_vendored/sklearn/preprocessing/_data.py

@@ -29,7 +29,7 @@ class StandardScaler(BaseEstimator, TransformerMixin):
     individual features do not more or less look like standard normally
     distributed data (e.g. Gaussian with 0 mean and unit variance).
 
-    Examples:
+    **Examples:**
 
         .. code-block::
 

third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py

@@ -17,6 +17,21 @@ class OneHotEncoder(BaseEstimator):
     Note that this method deviates from Scikit-Learn; instead of producing sparse
     binary columns, the encoding is a single column of `STRUCT<index INT64, value DOUBLE>`.
 
+    **Examples:**
+
+        Given a dataset with two features, we let the encoder find the unique
+        values per feature and transform the data to a binary one-hot encoding.
+
+        .. code-block::
+
+            from bigframes.ml.preprocessing import OneHotEncoder
+            import bigframes.pandas as bpd
+
+            enc = OneHotEncoder()
+            X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
+            enc.fit(X)
+            print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
+
     Args:
         drop (Optional[Literal["most_frequent"]], default None):
             Specifies a methodology to use to drop one of the categories per feature.
@@ -37,21 +52,6 @@ class OneHotEncoder(BaseEstimator):
             when considering infrequent categories. If there are infrequent categories,
             max_categories includes the category representing the infrequent categories along with the frequent categories.
             Default None, set limit to 1,000,000.
-
-    Examples:
-
-        Given a dataset with two features, we let the encoder find the unique
-        values per feature and transform the data to a binary one-hot encoding.
-
-        .. code-block::
-
-            from bigframes.ml.preprocessing import OneHotEncoder
-            import bigframes.pandas as bpd
-
-            enc = OneHotEncoder()
-            X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
-            enc.fit(X)
-            print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
     """
 
     def fit(self, X, y=None):