Voting Classifier in Machine Learning

Original article was published by on Artificial Intelligence on Medium

Suppose you have trained a lot of classification models, and your every model is achieving the accuracy of 85 per cent. 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 sysassert sys.version_info >= (3, 5)# Scikit-Learn ≥0.20 is requiredimport sklearnassert sklearn.__version__ >= "0.20"# Common importsimport numpy as npimport os# to make this notebook's output stable across runsnp.random.seed(42)# To plot pretty figures%matplotlib inlineimport matplotlib as mplimport matplotlib.pyplot as pltmpl.rc('axes', labelsize=14)mpl.rc('xtick', labelsize=12)mpl.rc('ytick', labelsize=12)

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