Documentation added for class_weitghts to manage imbalance datasets.

Some explanations added how to use SKLL for imbalance datasets. 
Formatting-wise, I think you want to use the `.. note` directive, like this:

```rst
.. note:: 
    This option helps us to manage the case of imbalance data sets
    with the parameter ``class_weight`` for the classifiers: ``SVC``,
 
    ``LogisticRegression``, ``LinearSVC`` and ``SGDClassifier``.

    Two possible options are available. The first one with an 'auto' option that
    uses the values of y (the classes) to automatically adjust weights inversely
    proportional to class frequencies, as shown in the following code:

    .. code-block:: python

        {'class_weight': 'auto'}

    And the second option allows you to assign an specific weight per each class.
    The default weight per class is 1. An example could be:

    .. code-block:: python

        {'class_weight': {1: 10}}

    Additional examples and information can be seen `here <http://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_classes.html>`_.