Definition
Regression models map inputs to real-valued outputs. Linear regression is the classic starting point; tree ensembles and neural nets handle nonlinear relationships.
Evaluation uses MAE, MSE, RMSE, or R² rather than classification accuracy.
In simple terms
Classification asks "what kind?"; regression asks "how much?", like estimating a house price instead of labeling it luxury or budget.
Where you see it
- Forecasting demand or energy usage.
- Predicting reading time or model latency.
- Estimating property values from features.
How it works
1.Collect numeric targets
Each example has a continuous label.
2.Fit a model
Minimize error between predicted and true values.
3.Evaluate residuals
Check how wrong predictions are on held-out data.
Why it matters
- Many business and scientific questions need numbers, not categories, regression is the tool.
Often confused
Regression only means linear regression.
Any model that predicts continuous values is regression, including deep nets.