Regression

Introduction to Regression

Regression is a type of supervised machine learning used to predict a continuous value (like price, temperature, or score) based on one or more input features. It helps us understand and model the relationship between variables.

Common uses:

Predicting house prices
Forecasting sales or demand
Estimating physical measurements

Types of Regression Models

There are several types of regression models, each with its own strengths and use cases. Here are some of the most common:

Model	When to Use	Key Feature
Linear Regression	Relationship is roughly a straight line	Simple, interpretable
Polynomial Regression	Relationship is curved/non-linear	Fits curves by adding powers
Ridge Regression	Many features, risk of overfitting	Penalizes large weights (L2)
Lasso Regression	Many features, want feature selection	Can set some weights to zero (L1)

Key Differences

Linear Regression: Fits a straight line. Good for simple, linear relationships.
Polynomial Regression: Fits a curve by adding powers of features. Good for non-linear data.
Ridge Regression: Like linear regression, but adds a penalty for large weights (L2 regularization). Helps when you have many features or multicollinearity.
Lasso Regression: Like ridge, but can set some weights to zero (L1 regularization). Useful for feature selection and simpler models.

Visual Comparison

Summary Table

Model	Handles Non-Linearity	Reduces Overfitting	Feature Selection
Linear Regression	No	No	No
Polynomial Regression	Yes	No	No
Ridge Regression	No	Yes (L2)	No
Lasso Regression	No	Yes (L1)	Yes

Choosing the Right Model

Start with linear regression for simple problems.
Use polynomial regression if your data looks curved.
Try ridge regression if you have many features or your model overfits.
Use lasso regression if you want to automatically ignore unimportant features.

Reference this page for a quick overview and comparison of regression models!