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Quiz on KNN

Quiz on KNN

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Question 1 of 10

1. Question

What does “k” represent in k-NN?

Number of input features
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Number of classes
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Number of nearest neighbors to consider
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Number of trees
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Question 2 of 10

2. Question

k-NN is an example of which type of Machine Learning?

Unsupervised Learning
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Supervised Learning
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Reinforcement Learning
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Semi-Supervised Learning
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Question 3 of 10

3. Question

What is the main idea behind k-NN?

Find the farthest data point
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Predict using decision rules
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Use the most similar neighbors to classify or predict
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Train a deep learning model
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Question 4 of 10

4. Question

Which of the following is true about the k-NN algorithm?

It needs a training phase
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It is model-based
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It stores the entire training data
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It requires gradient descent
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Question 5 of 10

5. Question

What type of algorithm is k-NN commonly used for?

Only classification
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Only regression
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Both classification and regression
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Only clustering
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Question 6 of 10

6. Question

Which distance metric is commonly used in k-NN?

Jaccard Index
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Cosine Similarity
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Euclidean Distance
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Hamming Distance
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Question 7 of 10

7. Question

What happens if you choose a very large value of ‘k’?

Overfitting
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Underfitting
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Fast computation
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Perfect accuracy
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Question 8 of 10

8. Question

When is k-NN likely to perform poorly?

When features are normalized
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When data is very small
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When data has many irrelevant features or high dimensions
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When there are only two classes
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Question 9 of 10

9. Question

What is a key drawback of k-NN?

It’s difficult to implement
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It requires a lot of training
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It is computationally expensive at prediction time
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It doesn’t work for classification
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Question 10 of 10

10. Question

Which of the following best describes the behavior of k-NN?

Learns weights through training
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Builds a model first and then predicts
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Lazy learner that predicts based on similarity
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Uses neural networks to predict outcomes
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