In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to                       

Related MCQs

• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn't work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn’t work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to
• In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to                    
• In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
• In a linear regression problem, we are using �R-squared� to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
• In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
• We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation. Which of the following is/are true about Normal Equation? 1. We don't have to choose the learning rate 2. It becomes slow when number of features is very large 3. No need to iterate
• We can also compute the coefficient of linear regression with the help of an analytical method called �Normal Equation�. Which of the following is/are true about �Normal Equation�?1. We don�t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
• We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Which of the following is/are true about “Normal Equation”? 1. We don’t have to choose the learning rate 2. It becomes slow when number of features is very large 3. No need to iterate