K-Nearest Neighbors Quiz

What does the "K" in K-Nearest Neighbors represent?

• The number of neighbors to consider for classification

• The number of features in the dataset

• The number of classes in the dataset

• The distance metric used in the algorithm

Which of the following distance metrics is commonly used in the KNN algorithm?

• Euclidean distance

• Manhattan distance

• Minkowski distance

• All of the above

In KNN, what happens when you increase the value of K?

• The model becomes more sensitive to noise

• The decision boundary becomes smoother

• The model tends to overfit

• The model becomes faster

Which of the following is a key characteristic of the KNN algorithm?

• It is a parametric algorithm

• It assumes a linear relationship between features

• It requires a training phase before prediction

• It is a non-parametric algorithm

How does KNN handle ties when multiple classes have the same number of neighbors?

• It selects the class with the smallest distance

• It selects a class at random

• It increases the value of K to break the tie

• It ignores the tied neighbors and moves to the next closest neighbor

What is the time complexity of predicting a new data point in KNN?

• O(1)O(1)O(1)

• O(n)O(n)O(n)

• O(log⁡n)O(log n)O(logn)

• O(n2)O(n^2)O(n2)

Which of the following is a disadvantage of the KNN algorithm?

• It is difficult to interpret the results

• It requires a large amount of memory

• It cannot handle categorical data

• It is only suitable for linear problems

What type of learning method does the KNN algorithm belong to?

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Semi-supervised learning

Which of the following techniques can be used to optimize the performance of the KNN classifier?

• Dimensionality reduction

• Feature scaling

• Using weighted KNN

• All of the above

In a KNN classifier, what happens if the value of K is too small (e.g., K = 1)?

• The model is more likely to overfit

• The model will generalize better

• The model will underfit

• The model will ignore the outliers