--- a
+++ b/Stats/_LogisticRegressionCV.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+#
+# Copyright 2017 University of Westminster. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" It is an interface for the 'LogisticRegressionCV' training model (Logistic Regression with Cross-Validation).
+"""
+
+from typing import Dict, List, Any, TypeVar
+from Stats.Stats import Stats
+from sklearn import linear_model
+
+PandasDataFrame = TypeVar('DataFrame')
+SklearnLogisticRegressionCV = TypeVar('LogisticRegressionCV')
+
+__author__ = "Mohsen Mesgarpour"
+__copyright__ = "Copyright 2016, https://github.com/mesgarpour"
+__credits__ = ["Mohsen Mesgarpour"]
+__license__ = "GPL"
+__version__ = "1.1"
+__maintainer__ = "Mohsen Mesgarpour"
+__email__ = "mohsen.mesgarpour@gmail.com"
+__status__ = "Release"
+
+
+class _LogisticRegressionCV(Stats):
+    def __init__(self):
+        """Initialise the objects and constants.
+        """
+        super(self.__class__, self).__init__()
+        self._logger.debug(__name__)
+        self._logger.debug("Run Logistic Regression with Cross-Validation.")
+
+    def train(self,
+              features_indep_df: PandasDataFrame,
+              feature_target: List,
+              model_labals: List=[0, 1],
+              **kwargs: Any) -> SklearnLogisticRegressionCV:
+        """Perform the training, using the Logistic Regression with Cross-Validation.
+        :param features_indep_df: the independent features, which are inputted into the model.
+        :param feature_target: the target feature, which is being estimated.
+        :param model_labals: the target labels (default [0, 1]).
+        :param kwargs: Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs',
+        tol=0.0001, max_iter=100, class_weight=None, n_jobs=-1, verbose=0, refit=True, intercept_scaling=1.0,
+        multi_class='ovr', random_state=None
+        :return: the trained model.
+        """
+        self._logger.debug("Train " + __name__)
+        model_train = linear_model.LogisticRegressionCV(**kwargs)
+        model_train.fit(features_indep_df, feature_target)
+        return model_train
+
+    def train_summaries(self,
+                        model_train: SklearnLogisticRegressionCV) -> Dict:
+        """Produce the training summary.
+        :param model_train: the instance of the trained model.
+        :return: the training summary.
+        """
+        self._logger.debug("Summarise " + __name__)
+        summaries = dict()
+        # Coefficient of the features in the decision function.
+        summaries["coef_"] = model_train.coef_
+        # Intercept (a.k.a. bias) added to the decision function.
+        summaries["intercept_"] = model_train.intercept_
+        # Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element.
+        summaries["n_iter_"] = model_train.n_iter_
+        return summaries
+
+    def plot(self,
+             model_train: SklearnLogisticRegressionCV,
+             feature_names: List,
+             class_names: List=["True", "False"]):
+        """Plot the tree diagram.
+        :param model_train: the instance of the trained model.
+        :param feature_names: the names of input features.
+        :param class_names: the predicted class labels.
+        :return: the model graph.
+        """
+        self._logger.debug("Plot " + __name__)
+        # todo: plot
+        pass
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