Top 10 Regression Questions and Answers
1. What is regression in machine learning?
Regression is a supervised machine learning technique that is used to predict continuous values based on input features.
2. What are the different types of regression?
Some common types of regression include linear regression, logistic regression, polynomial regression, and ridge regression.
3. What is the difference between linear and logistic regression?
Linear regression is used for predicting continuous values, while logistic regression is used for binary classification problems.
4. What is the purpose of regularization in regression?
Regularization is used to prevent overfitting in regression models by adding a penalty term to the loss function.
5. How do you evaluate the performance of a regression model?
Common metrics for evaluating regression models include mean squared error, r-squared value, and root mean squared error.
6. What is multicollinearity in regression?
Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other.
7. How do you handle missing values in regression?
Missing values can be handled by imputing them with the mean, median, or mode of the data, or by using advanced techniques like KNN imputation.
8. What is the difference between feature selection and feature extraction in regression?
Feature selection involves selecting a subset of relevant features from the original set, while feature extraction creates new features from the existing ones.
9. What is the bias-variance tradeoff in regression?
The bias-variance tradeoff refers to the balance between the error due to bias and the error due to variance in a regression model. A model with high bias underfits the data, while a model with high variance overfits the data.
10. How can you improve the performance of a regression model?
Some ways to improve the performance of a regression model include feature scaling, hyperparameter tuning, and using ensemble methods like random forests or gradient boosting.
