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• Binary Classification: It refers to classification problems/tasks that have only two class labels. For example, Email
spam detection (spam or not), churn prediction (whether customers will stop doing business on a particular website).
In general, binary classification tasks involve 2 labels—normal and abnormal. For example, "no-spam" is a normal
condition and "spam" is an abnormal condition. Another example is that "tumour not detected" is a normal state
that includes medical testing and "tumour detected" is an abnormal condition.
• Multi-class Classification: It implies those classification tasks that have more than two class labels. For example,
face classification, animal species classification, optical character recognition. In contrast to binary classification,
multiclass classification does not have the concept of normal and abnormal classes. Instead, the examples are
classified as belonging to one of the several known classes.
The number of classes may be very large in some problems. For example, a model may tag a photo as belonging to one
among thousands of faces in a face recognition system. Text translation models are also a special type of multi-class
classification.
Binary Classification
As stated earlier, binary classification tasks have two class labels i.e. only two possible outcomes. The normal state is
assigned the class label 0 and the abnormal state is assigned the class label 1. For example, the following shows a graph
for classifying areas in a city based on covid/non-covid zones:
8 0
1
6
4
2
0
–2
–4
–6
–12 –10 –8 –6 –4 –2 0 2
Several algorithms are used for binary classification. These include:
• Logistic Regression
• k-Nearest Neighbors
• Decision Trees
• Support Vector Machine
Out of these algorithms, let us study about ‘Logistic Regression’.
Classification & Clustering 301

