10000 Fix misuse of it's · rhiever/scikit-learn@ca4c727 · GitHub
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Fix misuse of it's
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‎doc/modules/label_propagation.rst

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

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

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

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

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

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

‎doc/tutorial/statistical_inference/supervised_learning.rst

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

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

‎sklearn/neighbors/nearest_centroid.py

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

‎sklearn/preprocessing/imputation.py

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