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.
This test primarily focus on following concepts related with 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 includes some of the following:
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.
- Logistic regression classifier is also called as softmax classifier owing to the manner in which it classifies the data in multiple classes using 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 cross-entropy loss function. In other words, the weights of logistic regression classifier is 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 log of maximum likelihood function and applying negative to log loss function in order to use gradient descent for optimization purpose. This is why cross-entropy loss function is also called as log loss function.
- Examples of problems where logistic regression can be used is 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
Logistic regression is used to predict _________ valued output?
How much marks a student can get in a competitive exam based on hours of study can be solved using _________ regression model
Logistic regression is _________ when the observed outcome of dependent variable can have only two values such as 0 and 1 or success and failure
Whether a student will pass or fail in the competitive exam based on hours of study can be solved using _________ regression model
________ regression can be termed as a special case of _________ regression when the outcome variable is categorical
In logistic regression, the goal is to predict _________
Which of the following can be used to evaluate the performance of logistic regression model?
Which of the following is link function in logistic regression
Logistic regression is _________ when the observed outcome of dependent variable can have multiple possible types
In logistic regression, following technique is used to measure the goodness of the fit
Which of the following metrics is equal to True Positive / (True positive + False Positive)
Which of the following metrics is equal to True Positive / (True positive + False Negative)
Logistic Regression uses Softmax function for which of the following?
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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