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School
The Hong Kong University of Science and Technology**We aren't endorsed by this school
Course
COMP 4331
Subject
Computer Science
Date
Dec 17, 2024
Pages
1
Uploaded by DeaconWalrusMaster409
4. (1 point) Select one. Imagine you are using a k-Nearest Neighbor classifier on a data set with lots of noise. You want your classifier to be less sensitive to the noise. Which is more likely to help and with what side-effect? O Increase the value of k => Increase in prediction time (O Decrease the value of k => Increase in prediction time O Increase the value of k => Decrease in prediction time (O Decrease the value of k => Decrease in prediction time Increase the value of k => Increase in prediction time 5. (1 point) Select all that apply: Identify the correct relationship between bias, vari- ance, and the hyperparameter k in the k-Nearest Neighbors algorithm: O Increasing k leads to increase in bias O Decreasing k leads to increase in bias O Increasing k leads to increase in variance O Decreasing k leads to increase in variance A and D 6. Consider a training dataset for a regression task as follows. D={(=",5), (®,5?) -, (@™, y™)} with z® € R and y € R. For regression with k-nearest neighbor, we make predictions on unseen data points sim- ilar to the classification algorithm, but instead of a majority vote, we take the mean of the output values of the k nearest points to some new data point z. That is, o) =1 3 4 ieN (z,D) where N (z, D) is the set of indices of the k closest training points to .
