Ivy_Honeywell_Data Analytics_4.2

Suppose you have been given a fair coin and you want to find out the odds of getting heads. Which of the following option is true for such a case?

• odds will be 0

• odds will be 0.5

• odds will be 1

• None of the above

Which of the following is true regarding the logistic function for any value “x”?

Note

Logistic(x): is a logistic function of any number “x”

Logit(x): is a logit function of any number “x”

Logit_inv(x): is an inverse logit function of any number “x”

• Logistic(x) = Logit(x)

• Logistic(x) = Logit_inv(x)

• Logit_inv(x) = Logit(x)

• None of these

Suppose you applied a logistic regression model on a given data and got a training accuracy X and testing accuracy Y. Now you want to add few new features in the same data. Select the option(s) which is/are correct in such a case, considering remaining parameters are same.

• Training accuracy increases

• Testing accuracy increases

• Training and testing accuracy increases or remains the same

Choose which of the following options is true regarding one-vs-all method in Logistic Regression.

• We need to fit n models in n-class classification problem

• We need to fit n-1 classification models to classify into n classes

• We need to fit only one model to classify into n classes

• None of the above

Is Logistic Regression a supervised machine learning algorithm?

• TRUE

• FALSE

Is it possible to design a logistic regression algorithm using a Neural Network Algorithm?

• TRUE

• FALSE

Which of the following methods do we use to best fit the data in Logistic Regression?

• Least Squarred Error

• Maximum Likelihood

• Jaccard Distance

• Both A and B

Which of the following algorithms do we use for variable selection?

• Lasso

• Ridge

• Both

• None of the above

The logit function (given as I(x)) is the log of odds function. What could be the range of logit function in the domain x= [0, 1]?

• (-infinity, infinity)

• (0,1)

• (0,infinity)

• (-infinity, 0)

Which of the following option is true?

• Linear Regression error values has to be normally distributed but in case of Logistic Regression it is not the case

• Logistic Regression error values has to be normally distributed but in case of Linear Regression it is not the case

• Both Linear and Logistic Error values have to be normally distributed

• Both Linear and Logistic Error values have not to be normally distributed