Classification and Regression in Machine Learning: Understanding the Difference

Author(s): Davide Nardini

Originally published on Towards AI.

Arguably, one of the most important concepts in machine learning is classification. This article will illustrate the difference between classification and regression in machine learning.

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If you’re new to the field of Machine Learning, you might find yourself a little confused about the distinction between Classification and Regression.

Don’t worry, you’re not alone. This misunderstanding is quite common, and it’s not challenging to resolve.

Before we start, please consider following me on Medium or LinkedIn. In this article, I’ve covered one of the most famous classification and regression algorithms in machine learning, namely the Decision Tree.

In this article, I’ll guide you through your first training session on a Machine Learning Algorithm: we’ll be training…

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Classification and Regression fall under Supervised Learning, a category in Machine Learning where we have prior knowledge of the target variable.

For instance, when predicting house prices, we use a dataset comprising input features such as square footage, number of rooms, etc., alongside a target variable: the house price.

In contrast, Unsupervised Learning occurs when we lack prior knowledge of the target variable. This often occurs in Cluster Analysis, where we identify clusters without prior information.

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There are other types of learning in Machine Learning, such as semi-supervised and… Read the full blog for free on Medium.

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Published via Towards AI