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Brainy Fact
One of the first algorithms used for machine learning was the Naive Bayes classifier. Spam
filtering systems used Naive Bayes till 2010.
Types of Classification
Classification is a supervised learning concept which groups a set of data into classes. It is mainly of four types, which
are as follows:
• 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 or he will continue). In general, binary classification x 2
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 and “tumour
detected” is an abnormal condition.
x 1
• Multiclass classification: It implies those classification tasks that
have more than two class labels. Each entity is assigned to one class
without any overlap. For example, face classification, animal species
classification, optical character recognition. In contrast to binary
x 2 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
x 1 models are also a special type of multiclass classification.
• Multi-label classification: Multi-label classification is used when a
situation might belong to more than one class at the same time. This
implies that for a given input, the output may contain a collection of
labels instead of a single one. For example, document classification x 2
(where a document may be classified into various groups at the same
time, such as “science” and “technology”), and object detection in
images etc.
x 1
Machine Learning Algorithms 341

