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KNN Interview Questions and Answers
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Table of Content
- Identify the distance matrix used in KNN Algorithm___________
- ______ algorithm is widely used for different kinds of learnings because of its uncomplicated and easy to apply nature
- Identify the false statement according to KNN disadavantage_________
- ______in KNN is a parameter that refers to the number of nearest neighbours to include in the majority of the voting process.
- Choosing _______values for k can be noisy and will have a higher influence on the result.
- __________ is also a lazy learner because it doesn’t learn discriminative function from the training data.
- True/False. Is cross validation another way to choose k?
- Higher dimension problem in KNN Algorithm _________.
- The KNN algorithm can compete with the most accurate models because it makes________ predictions.
- True/False. Can KNN be used for both Classification and Regression?
- _______is to evaluate any technique in classification problem.
- In KNN, increase in dimension also leads to the problem of ________.
- __________is the KNN function used for KNN algorithm in R programming
- __________is the KNN function used for KNN algorithm in python programming
- How many metrics are available in KNN algorithm?
-
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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