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Machine Learning 101 Practice Test: K-Nearest Neighbor Algorithm and Nearest Neighbor Analysis
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The K-Nearest Neighbor (KNN) algorithm is a supervised machine learning algorithm that classifies and predicts data points. It's used in many machine learning applications, including recommendation engines, concept search, and pattern recognition.  Here are some steps for the KNN algorithm: Assign a value to K. Calculate the distance between the new data entry and all other existing data entries. Arrange the distances in ascending order. Find the K nearest neighbors to the new entry based on the calculated distances. Assign the class that is most common among the K nearest... Show more
Machine Learning 101 Practice Test: K-Nearest Neighbor Algorithm and Nearest Neighbor Analysis
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20 Questions

1. Which of the following statements is not supporting in defining k Nearest Neighbor as a lazy learning algorithm?
2. When we set K = 1 in kNN algorithm, the predictions become more stable.
3. Which of the following statements is not a feature of kNN?
4. Suppose k = 3 and the data point A’s 3-nearest-neighbours from the dataset are instances X, Y and Z. The table shows their classes and the distances computed. Then A’s predicted class using majority voting will be ‘Good’?
5. What is the Manhattan distance between a data point (9, 7) and a new query instance (3, 4)?
6. KNN performs well on imbalanced data.
7. Which of the following statements is not true about k Nearest Neighbor?
8. It is good to use kNN for large data sets.
9. When we set K = 1 in kNN algorithm, the predictions become more stable.
10. Setting large values of K in kNN is computationally inexpensive.
11. Which of the following statements is false about k-Nearest Neighbor algorithm?
12. In kNN too large value of K has a negative impact on the data points.
13. In kNN low K value is sensitive to outliers.
14. Which of the following statements is not supporting kNN to be a lazy learner?
15. Which of the following statements is not true about k-Nearest Neighbor classification?
16. Cross-validation is a smart way to find out the optimal K value.
17. Which of the following statements is not a feature of kNN?
18. Suppose k = 3 and the data point A’s 3-nearest-neighbours from the dataset are instances X, Y and Z. The table shows their classes and the distances computed. Then A’s predicted class using majority voting will be ‘Good’?
19. We have data from a survey and objective testing with two attributes A and B to classify whether a special paper tissue is good or not. Here are four training samples given in the table. Now the factory produces a new paper tissue that pass laboratory test with A = 3 and B = 7. If K = 3, then ‘Good’ is the classification of this new tissue?
20. Which of the following statements is not true about k Nearest Neighbor?

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