scikit-learn · ArturoAmorQ · Jan 24, 2024 · Jul 31, 2023 · Oct 16, 2023 · Jan 17, 2024
diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
@@ -104,6 +104,26 @@ be the average of the train subset, **not** the average of all the data. If the
 test subset is included in the average calculation, information from the test
 subset is influencing the model.
 
+How to avoid data leakage
+-------------------------
+
+Below are some tips on avoiding data leakage:
+
+* Always split the data into train and test subsets first, particularly
+  before any preprocessing steps.
+* Never include test data when using the `fit` and `fit_transform`
+  methods. Using all the data, e.g., `fit(X)`, can result in overly optimistic
+  scores.
+
+  Conversely, the `transform` method should be used on both train and test
+  subsets as the same preprocessing should be applied to all the data.
+  This can be achieved by using `fit_transform` on the train subset and
+  `transform` on the test subset.
+* The scikit-learn :ref:`pipeline <pipeline>` is a great way to prevent data
+  leakage as it ensures that the appropriate method is performed on the
+  correct data subset. The pipeline is ideal for use in cross-validation
+  and hyper-parameter tuning functions.
+
 An example of data leakage during preprocessing is detailed below.
 
 Data leakage during pre-processing
@@ -213,25 +233,6 @@ method is used during fitting and predicting::
     >>> print(f"Mean accuracy: {scores.mean():.2f}+/-{scores.std():.2f}")
     Mean accuracy: 0.46+/-0.07
 
-How to avoid data leakage
--------------------------
-
-Below are some tips on avoiding data leakage:
-
-* Always split the data into train and test subsets first, particularly
-  before any preprocessing steps.
-* Never include test data when using the `fit` and `fit_transform`
-  methods. Using all the data, e.g., `fit(X)`, can result in overly optimistic
-  scores.
-
-  Conversely, the `transform` method should be used on both train and test
-  subsets as the same preprocessing should be applied to all the data.
-  This can be achieved by using `fit_transform` on the train subset and
-  `transform` on the test subset.
-* The scikit-learn :ref:`pipeline <pipeline>` is a great way to prevent data
-  leakage as it ensures that the appropriate method is performed on the
-  correct data subset. The pipeline is ideal for use in cross-validation
-  and hyper-parameter tuning functions.
 
 .. _randomness:
 
@@ -413,7 +414,9 @@ it will allow the estimator RNG to vary for each fold.
     illustration purpose: what matters is what we pass to the
     :class:`~sklearn.ensemble.RandomForestClassifier` estimator.
 
+|details-start|
 **Cloning**
+|details-split|
 
 Another subtle side effect of passing `RandomState` instances is how
 :func:`~sklearn.base.clone` will work::
@@ -447,6 +450,8 @@ influence each other.
     :class:`~sklearn.ensemble.StackingClassifier`,
     :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).
 
+|details-end|
+
 CV splitters
 ............