--- a
+++ b/Stats/_RandomForestClassifier.py
@@ -0,0 +1,98 @@
+#!/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 'RandomForestClassifier' training model (Random Forest Classifier).
+"""
+
+from typing import Dict, List, Any, TypeVar
+from Stats.Stats import Stats
+from sklearn import ensemble
+from sklearn import tree
+import pydotplus
+
+PandasDataFrame = TypeVar('DataFrame')
+SklearnRandomForestClassifier = TypeVar('RandomForestClassifier')
+
+__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 _RandomForestClassifier(Stats):
+    def __init__(self):
+        """Initialise the objects and constants.
+        """
+        super(self.__class__, self).__init__()
+        self._logger.debug("Run Random Forest Classifier.")
+
+    def train(self,
+              features_indep_df: PandasDataFrame,
+              feature_target: List,
+              model_labals: List=[0, 1],
+              **kwargs: Any) -> SklearnRandomForestClassifier:
+        """Perform the training, using the Random Forest Classifier.
+        :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:  n_estimators=20, criterion='gini', max_depth=None, min_samples_split=100,
+        min_samples_leaf=50, min_weight_fraction_leaf=0.0, max_features='auto',
+        max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=-1, random_state=None,
+        verbose=0, warm_start=False, class_weight="balanced_subsample"
+        :return: the trained model.
+        """
+        self._logger.debug("Train " + __name__)
+        model_train = ensemble.RandomForestClassifier(**kwargs)
+        model_train.fit(features_indep_df.values, feature_target)
+        return model_train
+
+    def train_summaries(self,
+                        model_train: SklearnRandomForestClassifier) -> 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()
+        summaries['estimators_'] = model_train.estimators_
+        summaries['classes_'] = model_train.classes_
+        summaries['n_classes_'] = model_train.n_classes_
+        summaries['n_features_'] = model_train.n_features_
+        summaries['n_outputs_'] = model_train.n_outputs_
+        summaries['feature_importances_'] = model_train.feature_importances_
+        return summaries
+
+    def plot(self,
+             model_train: SklearnRandomForestClassifier,
+             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__)
+        for model_train_sub in model_train:
+            dot_data = tree.export_graphviz(model_train_sub, out_file=None, feature_names=feature_names,
+                                            class_names=class_names, filled=True, rounded=True,
+                                            special_characters=True)
+            yield pydotplus.graph_from_dot_data(dot_data)