What are classification datasets?

Classification datasets are collections of data used to train and evaluate machine learning models designed for classification tasks. In classification tasks, the goal is to predict the categorical labels of new instances based on the features provided. These datasets consist of input features (also called attributes or predictors) and corresponding categorical labels (also known as classes or targets).

Characteristics of Classification Datasets

1. Features: Numerical, categorical, or a mix of both, which are the independent variables used to predict the class labels.
2. Labels: Categorical outcomes or dependent variables that the model aims to predict. These can be binary (e.g., yes/no, spam/ham) or multi-class (e.g., species of flowers, types of fruits).
3. Size: Number of samples (rows) and number of features (columns). Larger datasets with more samples and features provide better training opportunities but also require more computational resources.
4. Balance: Class distribution within the dataset. A balanced dataset has approximately the same number of samples in each class, while an imbalanced dataset has a significant disparity in the number of samples across classes.

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.