Binary Classification is a type of classification model that have two label of classes. For example an email spam detection model contains two label of classes as spam or not spam. Most of the times the tasks of binary classification includes one label in a normal state, and another label in an abnormal state. In this article I will take you through Binary Classification in Machine Learning using Python.

I will be using the MNIST dataset, which is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “hello world” of Machine Learning.

Whenever people come up with new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns Machine Learning tackles this dataset sooner or later. So let’s import some libraries to start with our Binary Classification model:

# Python ≥3.5 is required
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)

Scikit-Learn provides many helper functions to download popular datasets. MNIST is one of them. The following code fetches the MNIST dataset:

from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 

#by aman kharwal #binary classification #data science #machine learning #python