Purpose of Machine Learning Models
The purpose of machine learning models is to learn from data to solve certain problems.
For example, they can be utilized to distinguish spam emails or to predict housing prices.
The types of problems that machine learning models address generally fall into two categories:
-
Predicting specific categories (classes)
Classification
-
Predicting continuous numerical values
Regression
Differences Between Classification and Regression
The differences between classification and regression problems are as follows:
Category | Classification | Regression |
---|---|---|
Output | Specific class (e.g., Spam/Normal ) | Continuous numeric value (e.g., $100,000 ) |
Examples | Cat vs. Dog | Height prediction in inches |
Objective | Grouping data | Predict numerical values |
When creating a machine learning model, it's important first to determine whether you're dealing with a classification
or regression
problem.
In the next lesson, we will explore classification
in more detail.
Mission
0 / 1
What is the most appropriate word for the blank below?
A regression problem involves predicting values.
continuous
discrete
integer
character
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