APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
True
|
|
|
False
|
|
|
Either A or B
|
|
|
None of the above
|
Detailed explanation-1: -True-False: Lasso Regularization can be used for variable selection in Linear Regression. True, In case of lasso regression we apply absolute penalty which makes some of the coefficients zero.
Detailed explanation-2: -Regularization in Linear Regression It means that our model works well not only with training or test data, but also with the data it’ll receive in the future. In summary, to achieve this, regularization shrinks the weights toward zero to discourage complex models.
Detailed explanation-3: -In linear regression, the relationship between the two variables is assumed to be linear. This answer is true.
Detailed explanation-4: -1. Which of the following is correct about regularized regression? Explanation: Regularized regression does not perform as well as random forest.