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Classification Model

The term ‘Classification’ refers to the process of finding
a model or function which helps in separating the data
into distinct discrete values. In the Classification model,
data is categorised under different labels according to the
parameters defined in the input, and then the labels are
predicted for the data. For example, students are classified
on the basis of the grades obtained in the examination.
This classification is based on the parameter ‘Marks’.
Classification models in Supervised Learning are designed to predict categorical outcomes. For example, in the
healthcare industry, these models are used to predict whether a patient has a disease (1) or not (0) based on
symptoms and medical history.

In emails, classification model filters the email as Spam or Inbox on the basis of clues or identifying patterns in
the input data. To identify whether an email is spam or not, classification models analyse numerous features that
can serve as clues. Once trained, the model analyses the clues in the email and decides: is this spam or not? It
assigns a category - “Spam” or “Not spam” - just like sorting your mail.

INBOX
SPAM

CLASSIFIER
SPAM FOLDER

SPAM

Kno
Knowledge Botwledge Bot
Gmail classifi ers help users navigate their inbox without being overwhelmed by irrelevant messages by
automatically sorting messages into categories such as “Promotions,” “Social,” or “Updates.”

Regression Model

Regression is the process of finding a model or function for differentiating the data into continuous values rather
than discrete values. Such models work on continuous data. For example, when forecasting financial statements
for a company, it may be useful to do a regression analysis to determine how changes in certain assumptions or
drivers of the business will impact revenue or expenses in the future. According to the analysis report, the model
would be trained.

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