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 with logistic regression would find these practice tests very useful. The goal for 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.
These test primarily focus on following concepts related with logistic regression:
- Types of logistic regression (Binomial, Multinomial, Ordinal)
- Logistic function, logit transformation
- Evaluation of logistic regression (AIC, Deviance calculations)
Other tests in this series includes some of the following:
Logistic Regression Concepts (Brushing up)
- Evaluation of Logistic regression models using some of the following techniques:
- Deviance calculations (Null and Residual Deviance)
- R-squared (McFadden, Cox and Snell, Likelihood ratio)
- ROC curve; AUC
- Hosmer Lemeshow tests
Practice Test
Deviance can be shown to follow __________
______ value of deviance represents the better fit of model
If the model deviance is significantly ________ than the null deviance then one can conclude that the predictor or set of predictors significantly improved model fit
Which of the following is analogous to R-Squared for logistic regression
Estimation in logistic regression chooses the parameters that ___________ the likelihood of observing the sample values
Which of the following tests can be used to assess whether the logistic regression model is well calibrated
ROC related with ROC curve stands for _______
Which of the following is used to identify the best threshold for separating positive and negative classes
ROC curve is a plot of __________ vs ___________
______ the value of AUC, better is the prediction power of the model
Share your Results:
In case you have not scored good enough, it may be good idea to go through basic machine learning concepts in relation with 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
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
- GPT Models In-context Learning: Examples - June 5, 2023
- OpenAI GPT-3 Models List: Explained with Examples - June 4, 2023
- Difference between Parametric vs Non-Parametric Models - June 3, 2023
Leave a Reply