Question

machine learning/ stats questions

1. Choose all the valid answers to the description about linear regression and logistic regression from the options below: A. Linear regression is an unsupervised learning problem; logistic regression is a super- vised learning problem. B. Linear regression deals with the prediction of co ontinuous values; logistic regression deals with the prediction of class labe C. We cannot use gradient descent to solve linear regression: we must resort to least square estimation to compute a closed-form solution. D. Linear regression is a convex optimization problem whereas logistic regression is not. E. None of the above is valid. 2. Choose all the valid answers to the description about gradient descent from the options below: A. The global minimum can always be reached by using gradient descent B. Every gradient descent iteration can always decrease the value of loss function even when the gradient of the loss function is zero C. When the learning rate is very large, some iterations of gradient descent may not decrease the value of loss function D. With different initial weights, the gradient descent algorithm may lead to different local minimum. E. None of the above is valid.

Answer 1

1.

A) Linear and Logistic both are supervised learning algorithms

Hence Invalid statement

B) Valid

C)Invalid

We can use gradient descent also to solve linear regression

D) Invalid

Logistic regression is also convex optimization problem