Machine learning for humans, part 2.1: supervised learning

Abstract

Supervised learning is a method of machine learning where the computer learns to approximate the relationship between a given feature’s input and output by referring to a predefined dataset referred to as labeled data. Linear regression is a method for performing supervised learning, specifically focusing on predicting a continuous numerical target variable based on a set of input features. By leveraging labeled training data, minimizing error, and using techniques such as gradient descent, linear regression can build a model that makes predictions on new unseen data. To avoid overfitting, which occurs when a model is too specific to the training data, regularization can be used as a penalty for large coefficients in input features. – AI-generated abstract.