neurodata
diff --git a/‎doc/whats_new/v1.2.rst
Lines changed: 0 additions & 3 deletions b/‎doc/whats_new/v1.2.rst
Lines changed: 0 additions & 3 deletions
diff --git a/‎sklearn/tree/_criterion.pxd
Lines changed: 12 additions & 17 deletions b/‎sklearn/tree/_criterion.pxd
Lines changed: 12 additions & 17 deletions
diff --git a/‎sklearn/tree/_criterion.pyx
Lines changed: 28 additions & 15 deletions b/‎sklearn/tree/_criterion.pyx
Lines changed: 28 additions & 15 deletions
@@ -526,9 +526,6 @@ Changelog
 
 - |Enhancement| :func:`tree.plot_tree`, :func:`tree.export_graphviz` now uses
   a lower case `x[i]` to represent feature `i`. :pr:`23480` by `Thomas Fan`_.
-- |Enhancement| The :class:`tree.BaseDecisionTree` now checks for a ``BaseCriterion``
-  Cython class rather than a ``Criterion``, which is a new abstract Cython API for
-  tree Criterion. :pr:`24678` by :user:`Adam Li <adam2392>`.
 
 :mod:`sklearn.utils`
 ....................
 
@@ -17,12 +17,7 @@ from ._tree cimport UINT32_t         # Unsigned 32 bit integer
 
 
 cdef class BaseCriterion:
-    # This is an abstract interface for criterion. For example, a tree model could be
-    # either supervised, or unsupervised computing impurity on samples of covariates, or
-    # labels, or both.
-    # 
-    # The downstream class must implement functions to compute the impurity in current
-    # node and children nodes.
+    """Abstract interface for criterion."""
 
     # Internal structures
     cdef DOUBLE_t* sample_weight         # Sample weights
@@ -40,10 +35,7 @@ cdef class BaseCriterion:
     cdef double weighted_n_left          # Weighted number of samples in the left node
     cdef double weighted_n_right         # Weighted number of samples in the right node
 
-    # The criterion object is maintained such that left and right collected
-    # statistics correspond to samples[start:pos] and samples[pos:end].
-    
-    # Core methods for any criterion class to implement
+    # Core methods that criterion class _must_ implement.
     cdef int reset(self) nogil except -1
     cdef int reverse_reset(self) nogil except -1
     cdef int update(self, SIZE_t new_pos) nogil except -1
@@ -57,18 +49,21 @@ cdef class BaseCriterion:
     cdef double proxy_impurity_improvement(self) nogil
 
 cdef class Criterion(BaseCriterion):
-    # The supervised criterion computes the impurity of a node and the reduction of
-    # impurity of a split on that node using the distribution of labels in parent and
-    # children nodes. It also computes the output statistics
-    # such as the mean in regression and class probabilities in classification.
+    '''Interface for impurity criteria.'''
 
     # Internal structures
     cdef const DOUBLE_t[:, ::1] y        # Values of y
 
     # Methods
-    cdef int init(self, const DOUBLE_t[:, ::1] y, DOUBLE_t* sample_weight,
-                  double weighted_n_samples, SIZE_t* samples, SIZE_t start,
-                  SIZE_t end) nogil except -1
+    cdef int init(
+        self,
         const DOUBLE_t[:, ::1] y,
+        DOUBLE_t* sample_weight,
+        double weighted_n_samples,
+        SIZE_t* samples,
+        SIZE_t start,
+        SIZE_t end,
+    ) nogil except -1
 
 cdef class ClassificationCriterion(Criterion):
     """Abstract criterion for classification."""
 
@@ -9,6 +9,8 @@
 #          Fares Hedayati <fares.hedayati@gmail.com>
 #          Jacob Schreiber <jmschreiber91@gmail.com>
 #          Nelson Liu <nelson@nelsonliu.me>
+#          Adam Li <adam2392@gmail.com>
+#          Jong Shin <jshinm@gmail.com>
 #
 # License: BSD 3 clause
 
@@ -30,15 +32,21 @@ from ._utils cimport WeightedMedianCalculator
 cdef double EPSILON = 10 * np.finfo('double').eps
 
 cdef class BaseCriterion:
-    """Abstract interface for any criterion.
+    """This is an abstract interface for criterion. For example, a tree model could
+    be either supervisedly, or unsupervisedly computing impurity on samples of
+    covariates, or labels, or both.
+
+    The downstream classes _must_ implement methods to compute the impurity
+    in current node and in children nodes.
 
     This object stores methods on how to calculate how good a split is using
     a set API. 
 
-    The criterion object is maintained such that left and right collected
-    statistics correspond to samples[start:pos] and samples[pos:end]. So the samples in
-    the "current" node is samples[start:end], while left and right children nodes are
-    split with the pointer 'pos' variable.
+    Samples in the "current" node are stored in `samples[start:end]` which is
+    partitioned around `pos` (an index in `start:end`) so that:
+
+       - the samples of left child node are stored in `samples[start:pos]`
+       - the samples of right child node are stored in `samples[pos:end]`
     """
     def __getstate__(self):
         return {}
@@ -173,9 +181,15 @@ cdef class BaseCriterion:
 cdef class Criterion(BaseCriterion):
     """Interface for impurity criteria.
 
-    This object stores methods on how to calculate how good a split is using
-    different metrics. This is the base class for any supervised tree criterion
-    model with a homogeneous float64 dtyped y.
+    The supervised criterion computes the impurity of a node and the reduction of
+    impurity of a split on that node using the distribution of labels in parent and
+    children nodes. It also computes the output statistics
+    such as the mean in regression and class probabilities in classification.
+
+    Instances of this class are responsible for compute splits' impurity difference
+
+    Criterion is the base class for criteria used in supervised tree-based models
+    with a homogeneous float64-dtyped y.
     """
     cdef int init(self, const DOUBLE_t[:, ::1] y, DOUBLE_t* sample_weight,
                   double weighted_n_samples, SIZE_t* samples, SIZE_t start,
@@ -188,19 +202,18 @@ cdef class Criterion(BaseCriterion):
         Parameters
         ----------
         y : array-like, dtype=DOUBLE_t
-            y is a buffer that can store values for n_outputs target variables
-        sample_weight : array-like, dtype=DOUBLE_t
-            The weight of each sample
+            y is a buffer that can store values for the `n_outputs` target variables
+        sample_weight : pointer to a buffer of DOUBLE_t
+            The pointer to the buffer storing each sample weight.
         weighted_n_samples : double
-            The total weight of the samples being considered
+            The sum of the weights of the samples being considered.
         samples : array-like, dtype=SIZE_t
             Indices of the samples in X and y, where samples[start:end]
             correspond to the samples in this node
         start : SIZE_t
-            The first sample to be used on this node
+            The index of first sample in `samples` to be considered in this node.
         end : SIZE_t
-            The last sample used on this node
-
+            The index of last sample in `samples` to be considered in this node.
         """
         pass