One Problems K Nearest Neighbor Learning Selecting Value K Say Given Following Data Set Bi Q43887633
One of the problems with k-nearest neighbor learning is selecting a value for k. Say you are given the following data set. This is a binary classification task in which the instances are described by two real-valued attributes. The labels or classes of each instance are denoted as either an asterisk or a circle. 6 o Xooooo oo OLLUTUUUUU 0 2 4 6 8 10 (a) What value of k minimizes training set error for this data set, and what is the resulting training set error? Why is training set error not a reasonable estimate of test set error, especially given this value of k? (b) What value of k minimizes the leave-one-out cross-validation error for this data set, and what is the resulting error? Why is cross-validation a better measure of test set performance? (c) What are the LOOCV errors for the lowest and highest k for this data set? Why might using too large or too small a value of k be bad? Show transcribed image text One of the problems with k-nearest neighbor learning is selecting a value for k. Say you are given the following data set. This is a binary classification task in which the instances are described by two real-valued attributes. The labels or classes of each instance are denoted as either an asterisk or a circle. 6 o Xooooo oo OLLUTUUUUU 0 2 4 6 8 10 (a) What value of k minimizes training set error for this data set, and what is the resulting training set error? Why is training set error not a reasonable estimate of test set error, especially given this value of k? (b) What value of k minimizes the leave-one-out cross-validation error for this data set, and what is the resulting error? Why is cross-validation a better measure of test set performance? (c) What are the LOOCV errors for the lowest and highest k for this data set? Why might using too large or too small a value of k be bad?
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