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

Related MCQs

• 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 rate2. It becomes slow when number of features is very large3. No need to iterate
• 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 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?
• To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, What do you expect will happen with the mean training error?
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?