[MRG] DOC Add dropdowns to Module 1.13 Feature Selection (scikit-learn#26662)

Author: greyisbetter

doc/modules/feature_selection.rst

@@ -201,31 +201,36 @@ alpha parameter, the fewer features selected.
 
 .. _compressive_sensing:
 
-.. topic:: **L1-recovery and compressive sensing**
-
-   For a good choice of alpha, the :ref:`lasso` can fully recover the
-   exact set of non-zero variables using only few observations, provided
-   certain specific conditions are met. In particular, the number of
-   samples should be "sufficiently large", or L1 models will perform at
-   random, where "sufficiently large" depends on the number of non-zero
-   coefficients, the logarithm of the number of features, the amount of
-   noise, the smallest absolute value of non-zero coefficients, and the
-   structure of the design matrix X. In addition, the design matrix must
-   display certain specific properties, such as not being too correlated.
-
-   There is no general rule to select an alpha parameter for recovery of
-   non-zero coefficients. It can by set by cross-validation
-   (:class:`~sklearn.linear_model.LassoCV` or
-   :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
-   under-penalized models: including a small number of non-relevant variables
-   is not detrimental to prediction score. BIC
-   (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
-   high values of alpha.
-
-   **Reference** Richard G. Baraniuk "Compressive Sensing", IEEE Signal
+|details-start|
+**L1-recovery and compressive sensing**
+|details-split|
+
+For a good choice of alpha, the :ref:`lasso` can fully recover the
+exact set of non-zero variables using only few observations, provided
+certain specific conditions are met. In particular, the number of
+samples should be "sufficiently large", or L1 models will perform at
+random, where "sufficiently large" depends on the number of non-zero
+coefficients, the logarithm of the number of features, the amount of
+noise, the smallest absolute value of non-zero coefficients, and the
+structure of the design matrix X. In addition, the design matrix must
+display certain specific properties, such as not being too correlated.
+
+There is no general rule to select an alpha parameter for recovery of
+non-zero coefficients. It can by set by cross-validation
+(:class:`~sklearn.linear_model.LassoCV` or
+:class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
+under-penalized models: including a small number of non-relevant variables
+is not detrimental to prediction score. BIC
+(:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
+high values of alpha.
+
+.. topic:: Reference
+
+   Richard G. Baraniuk "Compressive Sensing", IEEE Signal
    Processing Magazine [120] July 2007
    http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf
 
+|details-end|
 
 Tree-based feature selection
 ----------------------------
@@ -282,6 +287,10 @@ instead of starting with no features and greedily adding features, we start
 with *all* the features and greedily *remove* features from the set. The
 `direction` parameter controls whether forward or backward SFS is used.
 
+|details-start|
+**Detail on Sequential Feature Selection**
+|details-split|
+
 In general, forward and backward selection do not yield equivalent results.
 Also, one may be much faster than the other depending on the requested number
 of selected features: if we have 10 features and ask for 7 selected features,
@@ -299,16 +308,18 @@ cross-validation requires fitting `m * k` models, while
 :class:`~sklearn.feature_selection.SelectFromModel` always just does a single
 fit and requires no iterations.
 
-.. topic:: Examples
-
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
-
-.. topic:: References:
+.. topic:: Reference
 
    .. [sfs] Ferri et al, `Comparative study of techniques for
       large-scale feature selection
       <https://citeseerx.ist.psu.edu/doc_view/pid/5fedabbb3957bbb442802e012d829ee0629a01b6>`_.
 
+|details-end|
+
+.. topic:: Examples
+
+    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
+
 Feature selection as part of a pipeline
 =======================================