Doc Add dropdowns to 1.10.decision trees (scikit-learn#26699)

Author: Shreesha3112

doc/modules/tree.rst

@@ -146,6 +146,10 @@ Once trained, you can plot the tree with the :func:`plot_tree` function::
    :scale: 75
    :align: center
 
+|details-start|
+**Alternative ways to export trees**
+|details-split|
+
 We can also export the tree in `Graphviz
 <https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
 exporter. If you use the `conda <https://conda.io>`_ package manager, the graphviz binaries
@@ -212,6 +216,8 @@ of external libraries and is more compact:
     |   |   |--- class: 2
     <BLANKLINE>
 
+|details-end|
+
 .. topic:: Examples:
 
  * :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py`
@@ -281,7 +287,6 @@ of shape ``(n_samples, n_outputs)`` then the resulting estimator will:
   * Output a list of n_output arrays of class probabilities upon
     ``predict_proba``.
 
-
 The use of multi-output trees for regression is demonstrated in
 :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`. In this example, the input
 X is a single real value and the outputs Y are the sine and cosine of X.
@@ -303,16 +308,20 @@ the lower half of those faces.
 
 .. topic:: Examples:
 
- * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`
- * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
+  * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`
+  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
 
-.. topic:: References:
+|details-start|
+**References**
+|details-split|
 
  * M. Dumont et al,  `Fast multi-class image annotation with random subwindows
    and multiple output randomized trees
    <http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/DMWG09/dumont-visapp09-shortpaper.pdf>`_, International Conference on
    Computer Vision Theory and Applications 2009
 
+|details-end|
+
 .. _tree_complexity:
 
 Complexity
@@ -403,6 +412,10 @@ Tree algorithms: ID3, C4.5, C5.0 and CART
 What are all the various decision tree algorithms and how do they differ
 from each other? Which one is implemented in scikit-learn?
 
+|details-start|
+**Various decision tree algorithms**
+|details-split|
+
 ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan.
 The algorithm creates a multiway tree, finding for each node (i.e. in
 a greedy manner) the categorical feature that will yield the largest
@@ -428,6 +441,8 @@ it differs in that it supports numerical target variables (regression) and
 does not compute rule sets. CART constructs binary trees using the feature
 and threshold that yield the largest information gain at each node.
 
+|details-end|
+
 scikit-learn uses an optimized version of the CART algorithm; however, the
 scikit-learn implementation does not support categorical variables for now.
 
@@ -500,8 +515,9 @@ Log Loss or Entropy:
 
     H(Q_m) = - \sum_k p_{mk} \log(p_{mk})
 
-
-.. note::
+|details-start|
+Shannon entropy:
+|details-split|
 
   The entropy criterion computes the Shannon entropy of the possible classes. It
   takes the class frequencies of the training data points that reached a given
@@ -531,6 +547,8 @@ Log Loss or Entropy:
 
       \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m)
 
+|details-end|
+
 Regression criteria
 -------------------
 
@@ -671,7 +689,9 @@ be pruned. This process stops when the pruned tree's minimal
 
     * :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
 
-.. topic:: References:
+|details-start|
+**References**
+|details-split|
 
     .. [BRE] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification
       and Regression Trees. Wadsworth, Belmont, CA, 1984.
@@ -685,3 +705,5 @@ be pruned. This process stops when the pruned tree's minimal
 
     * T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical
       Learning, Springer, 2009.
+
+|details-end|