# Supervised ML: Regression (Data Science & ML)

> Linear regression, Ridge, Lasso, ElasticNet, metrics (MSE, RMSE, R²), overfitting, regularization

- 24 interview questions
- Mid-Level
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. 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.

## 2. 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.

## 3. 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.

## 21 more questions available

- 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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