Classification Datasets FAQs

What is a classification dataset?

A classification dataset is a collection of data points that are labeled into categories or classes. It is used to train machine learning models to classify new data into one of the predefined classes.

Why are classification datasets important?

They provide the necessary data to train and evaluate classification models, enabling the development of systems that can categorize data automatically based on learned patterns.

How do I choose the right classification dataset for my project?

Consider the domain of your project (e.g., medical, financial, image recognition), the size and quality of the dataset, the number of classes, and the relevance of the features to your specific problem.

Classification is a type of supervised learning where the objective is to predict the categorical labels of new instances based on past observations. The goal is to learn a model from the training data that can predict the class label for unseen data accurately. Classification problems are common in many fields such as finance, healthcare, marketing, and more. In this article we will discuss some popular datasets used for classification.