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A supervised learning approach modeling relationships to predict continuous numerical outcomes from input features.
DESCRIPTION
Regression estimates how a dependent variable changes with one or more predictors. Methods include linear, polynomial, and generalized linear models, among others. Fitting minimizes a loss, commonly squared error, yielding coefficients interpretable under model assumptions. Performance is evaluated by residual diagnostics and metrics like RMSE and R². Regularization and cross-validation help prevent overfitting and improve generalization.
ALTERNATIVE DEFINITIONS
Wikipedia
Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
Classical definition focusing on estimating relationships between variables.
NIST/SEMATECH e-Handbook of Statistical Methods
Regression models describe the relationship between a response variable and one or more predictor variables.
Emphasizes modeling relationships between response and predictors in applied settings.
RELATED TERMS
Linear Model Generalized Linear Model Least Squares Residuals R-squared Regularization Cross-Validation Mean Squared Error Bias-Variance Tradeoff Feature Engineering
COMPETING TERMS
RELATIONS
- Narrower: Isotonic Regression Lasso Regression Linear Regression Logistic Regression Multivariate Regression Nonlinear Regression Polynomial Regression Ridge Regression Robust Regression
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist Data Analyst AI Engineer
EXTERNAL SOURCES
- Wikipedia - Regression analysis
- scikit-learn - Linear models for regression
- Penn State Statistics Online - Introduction to regression analysis
TOP QUESTIONS
When should I use Regression?
Use regression when the target variable is continuous and you need to model relationships or forecast values.
Category: Advice
What assumptions underlie linear Regression?
Linearity, independence, homoscedasticity, normal residuals, and correct model specification are common assumptions.
Category: Concept
How do I evaluate Regression performance?
Use metrics like RMSE, MAE, and R², and inspect residual plots for systematic errors.
Category: Validation
What is overfitting in Regression?
Overfitting occurs when the model captures noise; use regularization and cross-validation to mitigate it.
Category: Risk
How does Regularization relate to Regression?
L1 and L2 penalties shrink coefficients to reduce variance and improve generalization.
Category: Concept
What is multicollinearity in Regression?
Strong correlation among predictors inflates variance of estimates; use VIF, PCA, or regularization to address it.
Category: Mistakes
How do I select features for Regression?
Combine domain knowledge, correlation analysis, and techniques like Lasso or stepwise selection.
Category: Practical
Can Regression handle nonlinear relationships?
Yes, with polynomial terms, transformations, splines, or kernel methods.
Category: Advice
What is the difference between Regression and Classification?
Regression predicts continuous values; classification predicts discrete class labels.
Category: Comparison