💡 Regression Models

Regression Models#

Models that predict continuous numeric outcomes.

Important

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What it is#

Regression models predict a continuous number — a price, a temperature, a demand — rather than a class. They learn a function mapping features to a real-valued output, fitting a curve or surface through the data.

The landscape#

The simplest is linear regression (a weighted sum of features), extending to polynomial, regularized (ridge, lasso), tree-based (random forests, gradient boosting), and neural regressors. The same algorithm family often has both a classification and a regression form.

How they’re judged#

Regression is scored by how far predictions land from the truth — MSE / RMSE, MAE, and R² — and trained to minimize a distance-based loss. Because those errors use magnitudes, regression is sensitive to outliers, which is why robust losses and metrics exist.

Theme: AI & ML Concepts · All terminology

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Mind map — connected ideas

Classification Models · Linear Models · Mean Squared Error (MSE) · Loss Functions · Outlier · Neural Networks

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More in AI & ML Concepts

AI (Artificial Intelligence) · Classification Models · Computer Vision (CV) · Decision Trees · Linear Models · LLMs (Large Language Models) · Logistic Regression · Machine Learning (ML) · Medical AI · Natural Language Processing (NLP) · Neural Networks · Support Vector Machines (SVMs) · Target Variable