Consider the following two classification methods: k-Nearest Neighbour with k = 1 employing the Euclidean distance (1NN), and Logistic Regression (LR). Create and draw a 2D labelled data set on which the leave-one-out validation error of 1NN is zero, but the leave- one-out validation error of LR is positive.

Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
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7
Consider the following two classification
methods: k-Nearest Neighbour with k = 1
employing the Euclidean distance (1NN), and
Logistic Regression (LR). Create and draw a 2D
labelled data set on which the leave-one-out
validation error of 1NN is zero, but the leave-
one-out validation error of LR is positive.
Transcribed Image Text:Consider the following two classification methods: k-Nearest Neighbour with k = 1 employing the Euclidean distance (1NN), and Logistic Regression (LR). Create and draw a 2D labelled data set on which the leave-one-out validation error of 1NN is zero, but the leave- one-out validation error of LR is positive.
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