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

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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

- Mean Squared Error or R-Squared – Which one to use? - September 30, 2020
- Linear Regression Explained with Python Examples - September 30, 2020
- Correlation Concepts, Matrix & Heatmap using Seaborn - September 29, 2020