From 7f4d279dc3f679efd1900385919a3e34d83f2680 Mon Sep 17 00:00:00 2001
From: Preston Parry <ClimbsBytes@gmail.com>
Date: Sun, 28 Aug 2016 10:23:05 -0700
Subject: [PATCH] Minor docstring clarification (#6926)

---
 sklearn/ensemble/forest.py            | 42 +++++++++++++--------------
 sklearn/ensemble/gradient_boosting.py | 12 ++++----
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
index 65522064df..4d4f04bc12 100644
--- a/sklearn/ensemble/forest.py
+++ b/sklearn/ensemble/forest.py
@@ -160,9 +160,9 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
         Parameters
         ----------
         X : array-like or sparse matrix, shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -184,9 +184,9 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
         Parameters
         ----------
         X : array-like or sparse matrix, shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -217,9 +217,9 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The training input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csc_matrix``.
+            The training input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csc_matrix``.
 
         y : array-like, shape = [n_samples] or [n_samples, n_outputs]
             The target values (class labels in classification, real numbers in
@@ -516,9 +516,9 @@ class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest,
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -552,9 +552,9 @@ class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest,
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -605,9 +605,9 @@ class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest,
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -666,9 +666,9 @@ class ForestRegressor(six.with_metaclass(ABCMeta, BaseForest, RegressorMixin)):
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
 
         Returns
         -------
diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py
index 02e3765cfc..4ea8ef8e4e 100644
--- a/sklearn/ensemble/gradient_boosting.py
+++ b/sklearn/ensemble/gradient_boosting.py
@@ -1234,9 +1234,9 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble,
         Parameters
         ----------
         X : array-like or sparse matrix, shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will
+            be converted to a sparse ``csr_matrix``.
 
         Returns
         -------
@@ -1865,9 +1865,9 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
         Parameters
         ----------
         X : array-like or sparse matrix, shape = [n_samples, n_features]
-            The input samples. Internally, it will be converted to
-            ``dtype=np.float32`` and if a sparse matrix is provided
-            to a sparse ``csr_matrix``.
+            The input samples. Internally, its dtype will be converted to
+            ``dtype=np.float32``. If a sparse matrix is provided, it will
+            be converted to a sparse ``csr_matrix``.
 
         Returns
         -------
-- 
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