MNT Flake8 fix for (See the Travis failure)
I think there's concensus on not to introduce new flake8 errors
cc
@@ -14,9 +14,10 @@ influence of samples selected by the model as support vectors.
The ``C`` parameter trades off correct classification of training examples
against maximization of the decision function's margin. For larger values of
``C``, a smaller margin will be accepted if the decision function is better at
-classifying all training points correctly. A lower ``C`` will encourage a larger
-margin, therefore a simpler decision function, at the cost of training accuracy.
-In other words``C`` behaves as a regularization parameter in the SVM.
+classifying all training points correctly. A lower ``C`` will encourage a
+larger margin, therefore a simpler decision function, at the cost of training
+accuracy. In other words``C`` behaves as a regularization parameter in the
+SVM.
The first plot is a visualization of the decision function for a variety of
parameter values on a simplified classification problem involving only 2 input
@@ -49,15 +50,16 @@ centers of high density of any pair of two classes.
For intermediate values, we can see on the second plot that good models can
be found on a diagonal of ``C`` and ``gamma``. Smooth models (lower ``gamma``
values) can be made more complex by increasing the importance of classifying
-each point correctly (larger ``C`` values) hence the diagonal of good performing
-models.
+each point correctly (larger ``C`` values) hence the diagonal of good
+performing models.
Finally one can also observe that for some intermediate values of ``gamma`` we
-get equally performing models when ``C`` becomes very large: it is not necessary
-to regularize by enforcing a larger margin. The radius of the RBF kernel alone
-acts as a good structural regularizer. In practice though it might still be
-interesting to simplify the decision function with a lower value of ``C`` so as
-to favor models that use less memory and that are faster to predict.
+get equally performing models when ``C`` becomes very large: it is not
+necessary to regularize by enforcing a larger margin. The radius of the RBF
+kernel alone acts as a good structural regularizer. In practice though it
+might still be interesting to simplify the decision function with a lower
+value of ``C`` so as to favor models that use less memory and that are faster
+to predict.
We should also note that small differences in scores results from the random
splits of the cross-validation procedure. Those spurious variations can be
