Wrap lines

sambhav · sambhav · commit 06d4e099c574 · 2019-12-12T21:24:13.000Z
diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst
@@ -243,8 +243,8 @@ that it comes with a computational cost.
    `"Probability estimates for multi-class classification by pairwise coupling"
    <https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf>`_,
    JMLR 5:975-1005, 2004.
- 
- 
+
+
  * Platt
    `"Probabilistic outputs for SVMs and comparisons to regularized likelihood methods"
    <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_.
@@ -267,10 +267,11 @@ that sets the parameter ``C`` of class ``class_label`` to ``C * value``.
    :scale: 75
 
 
-:class:`SVC`, :class:`NuSVC`, :class:`SVR`, :class:`NuSVR`, :class:`LinearSVC`, :class:`LinearSVR` and
-:class:`OneClassSVM` implement also weights for individual samples in method
-``fit`` through keyword ``sample_weight``. Similar to ``class_weight``, these
-set the parameter ``C`` for the i-th example to ``C * sample_weight[i]``.
+:class:`SVC`, :class:`NuSVC`, :class:`SVR`, :class:`NuSVR`, :class:`LinearSVC`,
+:class:`LinearSVR` and :class:`OneClassSVM` also implement weights for
+individual samples in method ``fit`` through keyword ``sample_weight``. Similar
+to ``class_weight``, these set the parameter ``C`` for the i-th example to
+``C * sample_weight[i]``.
 
 
 .. figure:: ../auto_examples/svm/images/sphx_glr_plot_weighted_samples_001.png
@@ -392,10 +393,10 @@ Tips on Practical Use
   * **Setting C**: ``C`` is ``1`` by default and it's a reasonable default
     choice.  If you have a lot of noisy observations you should decrease it.
     It corresponds to regularize more the estimation.
-    
+
     :class:`LinearSVC` and :class:`LinearSVR` are less sensitive to ``C`` when
-    it becomes large, and prediction results stop improving after a certain 
-    threshold. Meanwhile, larger ``C`` values will take more time to train, 
+    it becomes large, and prediction results stop improving after a certain
+    threshold. Meanwhile, larger ``C`` values will take more time to train,
     sometimes up to 10 times longer, as shown by Fan et al. (2008)
 
   * Support Vector Machine algorithms are not scale invariant, so **it
@@ -412,7 +413,7 @@ Tips on Practical Use
     positive and few negative), set ``class_weight='balanced'`` and/or try
     different penalty parameters ``C``.
 
-  * **Randomness of the underlying implementations**: The underlying 
+  * **Randomness of the underlying implementations**: The underlying
     implementations of :class:`SVC` and :class:`NuSVC` use a random number
     generator only to shuffle the data for probability estimation (when
     ``probability`` is set to ``True``). This randomness can be controlled
@@ -547,7 +548,7 @@ correctly.  ``gamma`` defines how much influence a single training example has.
 The larger ``gamma`` is, the closer other examples must be to be affected.
 
 Proper choice of ``C`` and ``gamma`` is critical to the SVM's performance.  One
-is advised to use :class:`sklearn.model_selection.GridSearchCV` with 
+is advised to use :class:`sklearn.model_selection.GridSearchCV` with
 ``C`` and ``gamma`` spaced exponentially far apart to choose good values.
 
 .. topic:: Examples:
@@ -698,7 +699,7 @@ term :math:`\rho`
  * `"A Tutorial on Support Vector Regression"
    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.4288>`_,
    Alex J. Smola, Bernhard Schölkopf - Statistics and Computing archive
-   Volume 14 Issue 3, August 2004, p. 199-222. 
+   Volume 14 Issue 3, August 2004, p. 199-222.
 
 
 .. _svm_implementation_details:
@@ -722,5 +723,3 @@ computations. These libraries are wrapped using C and Cython.
 
     - `LIBLINEAR -- A Library for Large Linear Classification
       <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_.
-
-
