Fix misuse of it's

Author: yenchenlin

doc/modules/label_propagation.rst

@@ -55,7 +55,7 @@ data to some degree. The :class:`LabelPropagation` algorithm performs hard
 clamping of input labels, which means :math:`\alpha=1`. This clamping factor
 can be relaxed, to say :math:`\alpha=0.8`, which means that we will always
 retain 80 percent of our original label distribution, but the algorithm gets to
-change it's confidence of the distribution within 20 percent.
+change its confidence of the distribution within 20 percent.
 
 :class:`LabelPropagation` uses the raw similarity matrix constructed from
 the data with no modifications. In contrast, :class:`LabelSpreading`

doc/themes/scikit-learn/static/ML_MAPS_README.rst

@@ -14,7 +14,7 @@ the different items on the image upon mouseover.
 Modifying the map on the docs is currently a little bit tedious,
 so I'll try to make it as simple as possible.
 
-1. Editing the layout of the map and it's paths.
+1. Editing the layout of the map and its paths.
 ------------------------------------------------
 
 Use a Graphics editor like Inkscape Vector Graphics Editor

doc/themes/scikit-learn/static/nature.css_t

@@ -108,7 +108,7 @@ div.navbar ul li a:hover {
     /*The min-height is added here, to prevent the element from shrinking
     too much, while the scripts are still loading the search-bar.
     Without it, layout glitches occur, as the element keeps dynamically
-    changing it's size while its loading-contents adjusts into position.*/
+    changing its size while its loading-contents adjusts into position.*/
     min-height: 42px;
 }
 

doc/tutorial/machine_learning_map/ML_MAPS_README.txt

@@ -14,7 +14,7 @@ the different items on the image upon mouseover.
 Modifying the map on the docs is currently a little bit tedious,
 so I'll try to make it as simple as possible.
 
-1. Editing the layout of the map and it's paths.
+1. Editing the layout of the map and its paths.
 ------------------------------------------------
 
 Use a Graphics editor like Inkscape Vector Graphics Editor

doc/tutorial/statistical_inference/supervised_learning.rst

@@ -155,7 +155,7 @@ Linear regression
 .. currentmodule:: sklearn.linear_model
 
 :class:`LinearRegression`,
-in it's simplest form, fits a linear model to the data set by adjusting
+in its simplest form, fits a linear model to the data set by adjusting
 a set of parameters in order to make the sum of the squared residuals
 of the model as small as possible.
 

doc/tutorial/text_analytics/working_with_text_data.rst

@@ -393,7 +393,7 @@ with computer graphics.
   has many samples.
 
   By setting ``loss="hinge"`` and ``penalty="l2"`` we are configuring
-  the classifier model to tune it's parameters for the linear Support
+  the classifier model to tune its parameters for the linear Support
   Vector Machine cost function.
 
   Alternatively we could have used ``sklearn.svm.LinearSVC`` (Linear

sklearn/neighbors/nearest_centroid.py

@@ -106,7 +106,7 @@ def fit(self, X, y):
             raise ValueError("threshold shrinking not supported"
                              " for sparse input")
         check_classification_targets(y)
-        
+
         n_samples, n_features = X.shape
         le = LabelEncoder()
         y_ind = le.fit_transform(y)
@@ -115,7 +115,7 @@ def fit(self, X, y):
         if n_classes < 2:
             raise ValueError('y has less than 2 classes')
 
-        # Mask mapping each class to it's members.
+        # Mask mapping each class to its members.
         self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64)
         # Number of clusters in each class.
         nk = np.zeros(n_classes)

sklearn/preprocessing/imputation.py

@@ -281,7 +281,7 @@ def _dense_fit(self, X, strategy, missing_values, axis):
         # Most frequent
         elif strategy == "most_frequent":
             # scipy.stats.mstats.mode cannot be used because it will no work
-            # properly if the first element is masked and if it's frequency
+            # properly if the first element is masked and if its frequency
             # is equal to the frequency of the most frequent valid element
             # See https://github.com/scipy/scipy/issues/2636