Supervised ML: Regression
Linear regression, Ridge, Lasso, ElasticNet, metrics (MSE, RMSE, R²), overfitting, regularization
1What is the main objective of linear regression?
What is the main objective of linear regression?
Answer
Linear regression aims to model the relationship between a dependent variable (target) and one or more independent variables (features) by finding the straight line that minimizes the sum of squared errors. This technique allows predicting continuous values and forms the foundation for many more complex algorithms.
2In simple linear regression, what does the beta coefficient (β₁) represent?
In simple linear regression, what does the beta coefficient (β₁) represent?
Answer
The β₁ coefficient represents the slope of the regression line, indicating how much the target variable changes for a one-unit increase in the independent variable. A positive β₁ means a positive relationship, while a negative β₁ indicates an inverse relationship between variables.
3Which method is used to find the optimal coefficients in linear regression?
Which method is used to find the optimal coefficients in linear regression?
Answer
The Ordinary Least Squares (OLS) method minimizes the sum of squared residuals, meaning the difference between observed and predicted values. This approach provides a closed-form analytical solution and is the standard method for estimating linear regression parameters.
What does the coefficient of determination R² measure in regression?
What is the difference between MSE (Mean Squared Error) and RMSE (Root Mean Squared Error)?
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