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

### Practice Test

#### 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

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