Suppose you have trained a lot of classification models, and your each model is achieving the accuracy of 85 percent. A very simple way to create an even better classifier is to aggregate the predictions of each classifier and predict the class that gets the most votes. This majority-vote classification is known as a voting classifier.

In this article, I will take you through the voting classifier in Machine Learning. I will first start with importing the necessary libraries:

import sys
assert sys.version_info >= (3, 5)
​
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
​
# Common imports
import numpy as np
import os
​
# to make this notebook's output stable across runs
np.random.seed(42)
​
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

#python #data-science #machine-learning