Log Regression

Which of these are supervised machine learning algorithms?

• Linear Regression

• Logistic Regression

• K-Means Clustering

• All of these

Is it possible to design a linear and logistic regression using neural network?

• Yes

• No

• Cannot say

• May be possible

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

• Least Square Error

• Any distance metric

• Maximum Likelihood

• Least Square Error and Maximum Likelihood

Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

• AUC-ROC

• Accuracy

• Mean-Squared-Error

• None of these

Consider a following model for logistic regression: P (y =1|x, w)= g(w0 + w1x), where g(z) is the logistic function. In the above equation the P (y =1|x; w) , viewed as a function of x, that we can get by changing the parameters w.

What would be the range of p in such case?

• (0, inf)

• (-inf, 0 )

• (0, 1)

• (-inf, inf)

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 a few new features in the same data. Select the option(s) which is/are correct in such a case.

• Training accuracy increases

• Training accuracy increases or remains the same

• Testing accuracy decreases

• Testing accuracy increases or remains the same

Which of the following image is showing the cost function for y =1.

• 
• 
• Both

• None of these

Tags

CCSS.8.EE.B.5

Can a Logistic Regression classifier do a perfect classification on the above data?

• Yes

• No

• Cannot say

• None of these

Logistic regression is used when you want to:

• Predict a discrete variable from continuous or discrete variables.

• Predict a continuous variable from discrete variables.

• Predict a continuous variable from continuous or discrete variables.

• None of these

Logistic regression assumes that...

• The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.

• The independent variables are linearly related.

• Both of above

• None of these