Logistic Regression Interview Questions – Set 1

This page lists down the practice tests/interview questions and answers for Logistic regression in machine learning. Those wanting to test their machine learning knowledge in relation to logistic regression would find these practice tests very useful. The goal of these practice tests is to help you check your knowledge in logistic regression machine learning models from time to time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers/intern interviews in the area of machine learning would also find these practice tests/interview questions to be very helpful.

This test primarily focuses on the following concepts related to logistic regression:

• Introduction to logistic regression
• Logistic regression examples
• Types of logistic regression (Binomial, Multinomial, Ordinal)
• Link function
• Evaluation of logistic regression

Other tests in this series include some of the following:

• Logistic regression interview questions – Set 2
• Logistic regression interview questions – Set 3

You might also want to check a related post on Logistic regression titled – Training a logistic regression model using Python.

Logistic Regression Concepts (Brushing up)

• Logistic regression is used to estimate/predict the discrete-valued output such as success or failure, 0 or 1, etc.
• Logistic regression can be used for binary classification as well multinomial classification – classifying data in multiple classes.
• A logistic regression classifier is also called a softmax classifier owing to the manner in which it classifies the data in multiple classes using the softmax function. You may want to check out my post on What’s Softmax function and why do we need it?
• Logistic regression classifier is trained by applying gradient descent on the cross-entropy loss function. In other words, the weights of the logistic regression classifier are learned using gradient descent algorithm and cross-entropy loss function. You may want to check my post on Cross-entropy loss explained with Python examples.
• The cost function of logistic regression is derived from taking the log of the maximum likelihood function and applying negative to log loss function in order to use gradient descent for optimization purposes. This is why the cross-entropy loss function is also called a log loss function.
• Examples of problems where logistic regression can be used are whether a person is suffering from a specific disease or not; Or, a person is suffering from disease A, disease B, or disease C.
• Logistic regression can be types such as binomial, multinomial, and ordinal
• Logistic regression is used to estimate the probability of outcome dependent variable instead of actual value as like linear regression model.
• Logistic regression models are evaluated using metrics such as accuracy/precision / recall, AIC, Deviance calculations (Null and Residual/ Model deviance) ROC curve, etc. You may want to check out my post on classification models metrics – Accuracy, Precision, Recall, and F-Score

Practice Test

In case you have not scored well enough, it may be a good idea to go through basic machine learning concepts in relation to logistic regression. Following is the list of some good courses/pages:

• Logistic regression (Wikipedia)
• Logistic regression (PennState Eberly College of Science)
• Logistic regression
• Beginners guide in Logistic regression
• Understanding ROC curve
• Area under ROC curve