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Multinomial Regression Interview Questions and Answers

  • September 10, 2022
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Table of Content

  • In Multinomial Regression, the term multi refers to __________

    • a) Fixed number of outcomes.
    • b) Binary outcome.
    • c) More than one outcome.
    • d) More than two outcomes.

    Answer - d) More than two outcomes

  • In Multinomial Regression, the term nominal refers to ________

    • a) They are ordinal variables.
    • b) They are nominal variables.
    • c) They are normal variables.
    • d) None of the above.

    Answer - b) They are nominal variables

  • In Multinomial Regression, they are nominal outcome variables means ________

    • a) There is no order in the outcome.
    • b) There is order in outcome variable.
    • c) There is only two outcome variables.
    • d) There is only two outcome variables.

    Answer - a) There is no order in the outcome

  • The outcome variable is polytomous/ multiclass/ polychotomous logistic/ softmax regression/ multinomial logit/ maximum entropy classifier/ conditional maximum entropy model all of these mean -

    • a) Only one outcome variable.
    • b) Only two outcome variables.
    • c) More than two outcome variables.
    • d) None of the above.

    Answer - c) More than two outcome variables

  • At the center of the multinomial regression analysis is the task estimating the log odds of each category. If for example, k=n categories as the reference categories, the multinomial regression estimates __________ regression functions.

    • a) n.
    • b) n-1.
    • c) n-2.
    • d) n-3.

    Answer - b) n-1

  • Multinomial logistic regression is often considered an attractive analysis because; it does not assume normality, linearity, or homoscedasticity.

    • a) True.
    • b) False.
    • c) It assumes only normality.
    • d) None of the above.

    Answer - a) True

  • __________________ function from the nnet library in R is used for building multinomial regression models.

    • a) Multinorm.
    • b) Multinom.
    • c) Norm.
    • d) None of the above.

    Answer - b) Multinom

  • Multinomial regression is enhanced to ________________regression.

    • a) Simple linear.
    • b) Multi linear.
    • c) Logistic.
    • d) None of the above.

    Answer - c) Logistic

  • In R language, while building multinomial model __________function is used to change baseline level.

    • a) Relevel.
    • b) Level.
    • c) Bilevel.
    • d) None of the above.

    Answer - a) Relevel

  • In multinomial regression mathematical intuition, the equation of intercepts are calculated for different ______ with one considered baseline level.

    • a) Logit model.
    • b) Decision model.
    • c) Linear model.
    • d) None of the above.

    Answer - a) Logit model

  • In R language in order to compute probabilities values in multinomial regression we have to divide coefficients value by ________ .

    • a) Mean error.
    • b) Standard errors.
    • c) Mean coefficients.
    • d) None of the above.

    Answer - b) Standard errors

  • In Python ,when we are working with problems with more than two classes, you should specify the multi_class parameter of ________________________.

    • a) LogisticRegression.
    • b) LinearRegression.
    • c) Multilinear Regression.
    • d) None of the above.

    Answer - a) Logistic Regression

  • In Python, “Multinomial” class option is supported only by the _______________solvers.

    • a) Saga and liblinear.
    • b) Sag and lbfgs.
    • c) Newton-cg and sag.
    • d) Lbfgs and newton-cg.

    Answer - d) Lbfgs and newton-cg

  • In Python, for example -

    “model = LogisticRegression(solver='liblinear',c=0.05, multi_class='ovr',random_state=0)” ● 'ovr' says to make_____________________.
    • a) Without any fit for each class.
    • b) The binary fit for each class.
    • c) With only a single model fit.
    • d) None of the above.

    Answer - b) The binary fit for each class

  • In multinomial regression choose correct statement-

    I. In multinomial logistic regression, we use the concept of one vs rest classification using binary classification technique of logistic regression.
    II. Now, for example, let us have “K” classes. First, we divide the classes into two parts, “1 “represents the 1st class and “0” represents the rest of the classes, then we apply binary classification in this 2 class and determine the probability of the object to belong in 1st class vs rest of the classes.
    III. we apply this technique for the “k” number of classes and return the class with the highest probability. By, this way we determine in which class the object belongs. In this way multinomial logistic regression works
    • a) Only statement (I) is correct.
    • b) Only statement (II) is correct.
    • c) Only statement (II) is correct.
    • d) All of the above statements are correct.

    Answer - d) All of the above statements are correct

  • While doing multinomial regression, choose the list of things which we must check to ensure that the final output is valid from below statements –

    I. Your dependent variable must be Nominal. This does not mean that multinomial regression cannot be used for the ordinal variable. However, for multinomial regression, we need to run ordinal logistic regression.
    II. You must convert your categorical independent variables to dummy variables.
    III. There should be no multicollinearity.
    IV. There should be a linear relationship between the dependent variable and continuous independent variables. As we cannot measure this directly between nominal and continuous variables what we do is we take logit transformation of the dependent variable.
    V. Ensure that we do not have outliers and high influential points in the data.
    • a) Only statement (I).
    • b) Only statement (II).
    • c) Only statement (III).
    • d) All of the above 5 statements.

    Answer - d) All of the above 5 statements

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