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KNN Interview Questions and Answers

  • September 08, 2022
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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY 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

  • Identify the distance matrix used in KNN Algorithm___________

    • a) Euclidean Distance.
    • b) Cosine Similarity.
    • c) Manhattan Distance.
    • d) All.

    Answer - d) All

  • ______ algorithm is widely used for different kinds of learnings because of its uncomplicated and easy to apply nature

    • a) KNN.
    • b) Clustering.

    Answer - a) KNN

  • Identify the false statement according to KNN disadavantage_________

    • a) The cost of predicting the k nearest neighbours is very high.
    • b) Doesn’t work as expected when working with big number of features/parameters.
    • c) Hard to work with categorical features.
    • d) Feature engineering is not possible.

    Answer - a) The cost of predicting the k nearest neighbours is very high

  • ______in KNN is a parameter that refers to the number of nearest neighbours to include in the majority of the voting process.

    • a) K.
    • b) First N.
    • c) Second N.
    • d) All of the above.

    Answer - a) K.

  • Choosing _______values for k can be noisy and will have a higher influence on the result.

    • a) Smaller k value.
    • b) Standard k value.
    • c) Larger k value.
    • d) All of the above.

    Answer - a) Smaller k value

  • __________ is also a lazy learner because it doesn’t learn discriminative function from the training data

    • a) KNN.
    • b) Kmeans.

    Answer - a) KNN

  • True/False. Is cross validation another way to choose k?

    • a) True.
    • b) False.

    Answer - a) True

  • Higher dimension problem in KNN Algorithm _________

    • a) Nearest data points.
    • b) Curse of dimensionality.
    • c) Variable curse.

    Answer - a) Nearest data points

  • The KNN algorithm can compete with the most accurate models because it makes________ predictions.

    • a) Low accurate predictions.
    • b) Standard accurate.
    • c) High accurate predictions.

    Answer - a) Low accurate predictions

  • True/False. Can KNN be used for both Classification and Regression?

    • a) True.
    • b) False.

    Answer - a) True

  • _______is to evaluate any technique in classification problem.

    • a) Ease to interpret the output.
    • b) Calculation time of the algorithm.
    • c) Predictive Power.
    • d) All.

    Answer - a) Ease to interpret the output

  • In KNN, increase in dimension also leads to the problem of ________.

    • a) Underfitting.
    • b) Overfitting.

    Answer - a) Underfitting

  • __________is the KNN function used for KNN algorithm in R programming

    • a) KNN.
    • b) Kneighborsclassifier.

    Answer - a) KNN

  • __________is the KNN function used for KNN algorithm in python programming

    • a) KNN.
    • b) K neighborsclassifier.

    Answer - b) K neighbors classifier

  • How many metrics are available in KNN algorithm?

    • a) 3.
    • b) 2.
    • c) 4.
    • d) 1.

    Answer - a) 3

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