What is a Voting Classifier?

A Voting Classifier functions as a machine learning estimator that utilizes multiple base models or estimators. It aggregates their predictions to arrive at a final outcome.

When Should You Employ a Voting Classifier?

It is advisable to use a Voting Classifier when the models within the ensemble display comparable performance and tend to agree on their predictions.

Types of Voting Classifiers

1. Hard Voting: This approach selects the output class that receives the majority of votes from the base classifiers, meaning it opts for the class with the highest predicted probability.
2. Soft Voting: In contrast, soft voting determines the output class based on the average probabilities assigned to each class.

In essence, hard voting is applicable for models that output class labels, while soft voting is suited for those that generate class membership probabilities.

Python Implementation of Voting Classifier

Let's explore how to implement the Voting Classifier using Python's sklearn library.

# Importing necessary libraries

from sklearn.ensemble import VotingClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.svm import SVC

from sklearn.datasets import load_iris

from sklearn.metrics import accuracy_score

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

# Loading the Iris dataset

iris = load_iris()

X = iris.data[:, :4]

Y = iris.target

# Splitting the dataset into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)

# Creating an ensemble of models

estimators = []

estimators.append(('LR', LogisticRegression(solver='lbfgs', multi_class='multinomial', max_iter=200)))

estimators.append(('SVC', SVC(gamma='auto', probability=True)))

estimators.append(('DTC', DecisionTreeClassifier()))

# Implementing Voting Classifier with hard voting

voting_hard = VotingClassifier(estimators=estimators, voting='hard')

voting_hard.fit(X_train, y_train)

y_pred_hard = voting_hard.predict(X_test)

# Calculating accuracy for hard voting

hard_score = accuracy_score(y_test, y_pred_hard)

print("Hard Voting Score: %d" % hard_score)

# Implementing Voting Classifier with soft voting

voting_soft = VotingClassifier(estimators=estimators, voting='soft')

voting_soft.fit(X_train, y_train)

y_pred_soft = voting_soft.predict(X_test)

# Calculating accuracy for soft voting

soft_score = accuracy_score(y_test, y_pred_soft)

print("Soft Voting Score: %d" % soft_score)

The video titled "Ensemble Learning: Voting Classifier Explained with Practical Proofs" provides an in-depth look at how voting classifiers work, showcasing their practical applications.

Exploring the Voting Classifier in Scikit-Learn

The tutorial "Mastering Voting Classifier in Scikit-Learn: A Python Machine Learning Tutorial" is essential for anyone looking to deepen their understanding of implementing voting classifiers in Python.