def train(self, **kwargs):
self.train_kwargs = kwargs
test_size = 0.1
if kwargs.get('test_size') is not None:
test_size = kwargs.get('test_size')
del kwargs['test_size']
self._mc = ModelComparison()
self._mc.train_test_split(self.data,
test_size=test_size,
random_state=randint(2**16))
self._mc['RFC'] = RandomForestClassifier(**kwargs)
self.classifier = self._mc['RFC']
self._mc.fit()
return None
