Vaibhav Kumar has experience in the field of Data Science…

Ensemble learning techniques have a long record of showing better performance in a variety of machine learning applications. The domains of these applications include classification and regression problems. Random forest model and gradient boosting model are well-known ensemble models where they use a combination of weak learners to build up an ensemble. In these models, the collection of weak learners is homogeneous that means the same types of weak learners are grouped together to show their combined strength. In this article, we will show a heterogeneous collection of weak learners to build a hybrid ensemble learning model. Different types of machine learning algorithms are grouped together in this task to work on a classification problem. We will show the performance of individual weak learning models and then the performance of our hybrid ensemble model.

### What is Ensemble Learning?

The ensemble learning is a concept of machine learning where the combined power of machine learning models is employed in a learning problem such as a classification problem or a regression problem. In this approach, several homogeneous machine learning models are taken as weak learners and they are grouped together. When applied to the problem, each of the weak learners shows its own result either on the entire training set or on a fraction of the entire training set. Finally, the results of each weak learner are combined together to obtain the final result.

There are two popular categories of ensemble learning, one is called as the bagging and other is called the boosting. Random forest is the popular ensemble learning model that comes under the bagging category. AdaBoost is another popular ensemble learning model that comes under the boosting category. The bagging models work on a fraction of the entire dataset while the boosting models work on the entire dataset. For more details, please refer to the article A Primer to Ensemble Learning – Bagging and Boosting.

### Hybrid Ensemble Model

In this task, the five different types of machine learning models are used as weak learners to build a hybrid ensemble learning model. These models are – Logistic Regression Model, Decision Tree, Support Vector Machine, K-Nearest Neighbor Model, and the Naive Bayes Model. The term hybrid is used here because, in other ensemble models, a homogeneous collection of weak learners is used but in this task, a heterogeneous collection of weak learners is used.

The hybrid ensemble learning model, built using these weak learning models, is applied in the task of classification for the bank’s customer churn modelling.

### About the Dataset

The customer churn modelling dataset is used where the task is to predict the customer’s churn prediction for a bank. This dataset is publically available on Kaggle. This dataset comprises the bank’s customers details including the number of products the customer is availing, balance, estimated salary etc. It comprises the record of 10,000 customers with 13 attributes. The target attribute is a binary variable indicating whether the customer has exited from the bank or not.

### Performance of Weak Learners

Now let us have a look at the performance of every individual machine learning model in customer churn prediction. We have trained the five machine learning models including Logistic Regression Model, Decision Tree, Support Vector Machine, K-Nearest Neighbor Model, and the Naive Bayes Model on predicting the exit of customers from the bank. The classification accuracies have been compared using the confusion matrices of each of the models. For more validation, we have performed the 10-fold cross-validations and seen the accuracies of each of the five models.

Refer to the below python implementation for the above-mentioned purpose.

#Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

from sklearn.naive_bayes import GaussianNB

from sklearn.ensemble import VotingClassifier

from sklearn import model_selection

from sklearn.metrics import confusion_matrix

from sklearn. preprocessing import StandardScaler

from sklearn.model_selection import train_test_split

#Reading the dataset

dataset = pd.read_csv('Churn_Modelling.csv')

X = dataset.iloc[:, 3:13].values

y = dataset.iloc[:, 13].values

# Encoding categorical data

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X_1 = LabelEncoder()

X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])

labelencoder_X_2 = LabelEncoder()

X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])

from sklearn.compose import ColumnTransformer

t = ColumnTransformer([("Geography", OneHotEncoder(), [1])], remainder = 'passthrough')

X = ct.fit_transform(X)

X = X[:, 1:]

# Splitting the dataset into the Training set and Test set

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state = 0, stratify = y)

# Feature Scaling

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

#Defining the machine learning models

model1 = LogisticRegression()

model2 = DecisionTreeClassifier(max_depth = 2)

model3 = SVC()

model4 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

model5 = GaussianNB()

#Training the machine learning models

model1.fit(X_train, y_train)

model2.fit(X_train, y_train)

model3.fit(X_train, y_train)

model4.fit(X_train, y_train)

model5.fit(X_train, y_train)

#Making the prediction

y_pred1 = model1.predict(X_test)

y_pred2 = model2.predict(X_test)

y_pred3 = model3.predict(X_test)

y_pred4 = model4.predict(X_test)

y_pred5 = model5.predict(X_test)

#Confusion matrix

cm_LogisticRegression = confusion_matrix(y_test, y_pred1)

cm_DecisionTree = confusion_matrix(y_test, y_pred2)

cm_SupportVectorClass = confusion_matrix(y_test, y_pred3)

cm_KNN = confusion_matrix(y_test, y_pred4)

cm_NaiveBayes = confusion_matrix(y_test, y_pred5)

#10-fold cross-validation

kfold = model_selection.KFold(n_splits=10, random_state = 0)

result1 = model_selection.cross_val_score(model1, X_train, y_train, cv=kfold)

result2 = model_selection.cross_val_score(model2, X_train, y_train, cv=kfold)

result3 = model_selection.cross_val_score(model3, X_train, y_train, cv=kfold)

result4 = model_selection.cross_val_score(model4, X_train, y_train, cv=kfold)

result5 = model_selection.cross_val_score(model5, X_train, y_train, cv=kfold)

#Printing the accuracies achieved in cross-validation

print('Accuracy of Logistic Regression Model = ',result1.mean())

print('Accuracy of Decision Tree Model = ',result2.mean())

print('Accuracy of Support Vector Machine = ',result3.mean())

print('Accuracy of k-NN Model = ',result4.mean())

print('Accuracy of Naive Bayes Model = ',result5.mean())

#Output:-

**Hybrid Ensemble Learning Model**

In this work, we have defined each of the five machine learning models 5 times that results in a combination of a total of 25 weak learners. Then finally, then Max Voting Classifier method is used where the class which has been predicted mostly by the weak learners will be the final class prediction of the ensemble model.

#Defining Hybrid Ensemble Learning Model

# create the sub-models

estimators = []

#Defining 5 Logistic Regression Models

model11 = LogisticRegression(penalty = 'l2', random_state = 0)

estimators.append(('logistic1', model11))

model12 = LogisticRegression(penalty = 'l2', random_state = 0)

estimators.append(('logistic2', model12))

model13 = LogisticRegression(penalty = 'l2', random_state = 0)

estimators.append(('logistic3', model13))

model14 = LogisticRegression(penalty = 'l2', random_state = 0)

estimators.append(('logistic4', model14))

model15 = LogisticRegression(penalty = 'l2', random_state = 0)

estimators.append(('logistic5', model15))

#Defining 5 Decision Tree Classifiers

model16 = DecisionTreeClassifier(max_depth = 3)

estimators.append(('cart1', model16))

model17 = DecisionTreeClassifier(max_depth = 4)

estimators.append(('cart2', model17))

model18 = DecisionTreeClassifier(max_depth = 5)

estimators.append(('cart3', model18))

model19 = DecisionTreeClassifier(max_depth = 2)

estimators.append(('cart4', model19))

model20 = DecisionTreeClassifier(max_depth = 3)

estimators.append(('cart5', model20))

#Defining 5 Support Vector Classifiers

model21 = SVC(kernel = 'linear')

estimators.append(('svm1', model21))

model22 = SVC(kernel = 'poly')

estimators.append(('svm2', model22))

model23 = SVC(kernel = 'rbf')

estimators.append(('svm3', model23))

model24 = SVC(kernel = 'rbf')

estimators.append(('svm4', model24))

model25 = SVC(kernel = 'linear')

estimators.append(('svm5', model25))

#Defining 5 K-NN classifiers

model26 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

estimators.append(('knn1', model26))

model27 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

estimators.append(('knn2', model27))

model28 = KNeighborsClassifier(n_neighbors = 6, metric = 'minkowski', p = 2)

estimators.append(('knn3', model28))

model29 = KNeighborsClassifier(n_neighbors = 4, metric = 'minkowski', p = 1)

estimators.append(('knn4', model29))

model30 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 1)

estimators.append(('knn5', model30))

#Defining 5 Naive Bayes classifiers

model31 = GaussianNB()

estimators.append(('nbs1', model31))

model32 = GaussianNB()

estimators.append(('nbs2', model32))

model33 = GaussianNB()

estimators.append(('nbs3', model33))

model34 = GaussianNB()

estimators.append(('nbs4', model34))

model35 = GaussianNB()

estimators.append(('nbs5', model35))

# Defining the ensemble model

ensemble = VotingClassifier(estimators)

ensemble.fit(X_train, y_train)

y_pred = ensemble.predict(X_test)

#Confisuin matrix

cm_HybridEnsembler = confusion_matrix(y_test, y_pred)

#Cross-Validation

seed = 7

kfold = model_selection.KFold(n_splits=10, random_state=seed)

results = model_selection.cross_val_score(ensemble, X_train, y_train, cv=kfold)

print(results.mean())

#Output:-

As we can see the comparisons here, the hybrid ensemble learning model has outperformed all the individual learning model. We can verify this by observing the confusion matrices and cross-validation score of each individual model and the ensemble model. In future, we can make more hybrid ensemble models by adding more varieties of weak learners. We can also have a group of similar models with varying architectures.

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Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.