Minor docstring clarification (#6926)

scikit-learn · Aug 28, 2016 · 7f4d279 · 7f4d279
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commit 7f4d279
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diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
@@ -160,9 +160,9 @@ def apply(self, X):
         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 @@ def decision_path(self, X):
         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 @@ def fit(self, X, y, sample_weight=None):
         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 @@ def predict(self, X):
         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 in a leaf.
         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 @@ def predict_log_proba(self, X):
         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 @@ def predict(self, X):
         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
@@ -1234,9 +1234,9 @@ def apply(self, X):
         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 @@ def apply(self, X):
         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
         -------