Machine Learning (ML) MCQs | Page - 45

Q. 441) Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?

Q. 442) If Linear regression model perfectly first i.e., train error is zero, then

Q. 443) Which of the following methods do we use to find the best fit line for data in Linear Regression?

Q. 444) Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

Q. 445) 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 dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate

Q. 446) Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression

Q. 447) How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

Q. 448) In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

Q. 449) 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, was it a man?

Q. 450) For the given weather data, Calculate probability of playing