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Classification is a type of supervised learning where the model learns to assign input data to predefined categories or labels. The model is trained on labelled examples and then predicts which category new, unseen data belongs to.
Classification powers many everyday AI applications — from spam filters to medical diagnoses — making it one of the most widely used machine learning techniques.
Gmail's spam filter classifies incoming emails as either 'spam' or 'not spam' based on patterns it has learned from billions of previously labelled messages.
Supervised Learning
Supervised learning is a type of machine learning where the model is trained on labelled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict the correct output for new, unseen inputs.
Regression
Regression is a type of supervised learning where the model predicts a continuous numerical value rather than a category. It learns the relationship between input features and a continuous output variable from labelled training data.
Dataset
A dataset is a structured collection of data used to train, validate, or test a machine learning model. It can consist of text, images, numbers, audio, or any other type of information, typically organised into rows and columns or files and labels.
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