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An Introductory Guide to Data Science and Machine Learning

Types of Classification Problems

Explore the various types of classification problems in machine learning including binary, multi-class, multi-label, imbalanced, and multi-task classifications. Understand how these problem types differ, when to apply specific models, and strategies to handle challenges like class imbalance and multi-label scenarios, enabling you to select and implement appropriate approaches for effective classification tasks.

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Types of classification problems

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Types of Classification Problems

In classification, we work with problems where each instance is associated with a label or class. A classification model learns patterns from a labeled training dataset, where each example belongs to a specific class. The main categories of classification problems are:

Binary classification

In Binary classification problems, instances of data are labelled with two classes. The following problems fall under the umbrella of binary classification problems.

  • Animals in the picture can be recognized as cats or dogs.

  • Churn prediction, which involves detecting the customers who might cancel or renew a subscription to a Business.

  • Medical testing, which tells whether a person has a particular disease or not.

There are many famous classification models used for binary classification problems.

Multi-class classification

In multi-class classification ...