Minor docstring fixes to svm/classes.py

Author: Aerlinger

sklearn/svm/classes.py

@@ -15,8 +15,8 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
 
     Similar to SVC with parameter kernel='linear', but implemented in terms of
     liblinear rather than libsvm, so it has more flexibility in the choice of
-    penalties and loss functions and should scale better (to large numbers of
-    samples).
+    penalties and loss functions and should scale better to large numbers of
+    samples.
 
     This class supports both dense and sparse input and the multiclass support
     is handled according to a one-vs-the-rest scheme.
@@ -48,16 +48,16 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
         two classes.
         `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer`
         optimizes a joint objective over all classes.
-        While `crammer_singer` is interesting from an theoretical perspective
-        as it is consistent it is seldom used in practice and rarely leads to
-        better accuracy and is more expensive to compute.
+        While `crammer_singer` is as interesting from an theoretical perspective
+        as it is consistent, it is seldom used in practice and rarely leads to
+        better accuracy. It is also more expensive to compute.
         If `crammer_singer` is chosen, the options loss, penalty and dual will
         be ignored.
 
     fit_intercept : boolean, optional (default=True)
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be already centered).
 
     intercept_scaling : float, optional (default=1)
         When self.fit_intercept is True, instance vector x becomes
@@ -68,7 +68,7 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
         Note! the synthetic feature weight is subject to l1/l2 regularization
         as all other features.
         To lessen the effect of regularization on synthetic feature weight
-        (and therefore on the intercept) intercept_scaling has to be increased
+        (and therefore on the intercept) intercept_scaling has to be increased.
 
     class_weight : {dict, 'auto'}, optional
         Set the parameter C of class i to class_weight[i]*C for
@@ -94,7 +94,7 @@ class frequencies.
     coef_ : array, shape = [n_features] if n_classes == 2 \
             else [n_classes, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is a readonly property derived from `raw_coef_` that \
         follows the internal memory layout of liblinear.
@@ -105,9 +105,9 @@ class frequencies.
     Notes
     -----
     The underlying C implementation uses a random number generator to
-    select features when fitting the model. It is thus not uncommon,
+    select features when fitting the model. It is thus not uncommon
     to have slightly different results for the same input data. If
-    that happens, try with a smaller tol parameter.
+    that happens, try with a smaller `tol` parameter.
 
     The underlying implementation (liblinear) uses a sparse internal
     representation for the data that will incur a memory copy.
@@ -220,8 +220,8 @@ class LinearSVR(LinearModel, RegressorMixin):
 
     Similar to SVR with parameter kernel='linear', but implemented in terms of
     liblinear rather than libsvm, so it has more flexibility in the choice of
-    penalties and loss functions and should scale better (to large numbers of
-    samples).
+    penalties and loss functions and should scale better to large numbers of
+    samples.
 
     This class supports both dense and sparse input.
 
@@ -251,7 +251,7 @@ class LinearSVR(LinearModel, RegressorMixin):
     fit_intercept : boolean, optional (default=True)
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be already centered).
 
     intercept_scaling : float, optional (default=1)
         When self.fit_intercept is True, instance vector x becomes
@@ -281,7 +281,7 @@ class LinearSVR(LinearModel, RegressorMixin):
     coef_ : array, shape = [n_features] if n_classes == 2 \
             else [n_classes, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is a readonly property derived from `raw_coef_` that \
         follows the internal memory layout of liblinear.
@@ -466,7 +466,7 @@ class frequencies.
 
     coef_ : array, shape = [n_class-1, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is a readonly property derived from `dual_coef_` and
         `support_vectors_`.
@@ -590,7 +590,7 @@ class NuSVC(BaseSVC):
 
     coef_ : array, shape = [n_class-1, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is readonly property derived from `dual_coef_` and
         `support_vectors_`.
@@ -699,7 +699,7 @@ class SVR(BaseLibSVM, RegressorMixin):
 
     coef_ : array, shape = [1, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is readonly property derived from `dual_coef_` and
         `support_vectors_`.
@@ -809,7 +809,7 @@ class NuSVR(BaseLibSVM, RegressorMixin):
 
     coef_ : array, shape = [1, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is readonly property derived from `dual_coef_` and
         `support_vectors_`.
@@ -919,7 +919,7 @@ class OneClassSVM(BaseLibSVM):
 
     coef_ : array, shape = [n_classes-1, n_features]
         Weights assigned to the features (coefficients in the primal
-        problem). This is only available in the case of linear kernel.
+        problem). This is only available in the case of a linear kernel.
 
         `coef_` is readonly property derived from `dual_coef_` and
         `support_vectors_`