Question: 10 test points were classified using a 4-nearest neighbors algorithm. The actual y labels and the 4-NN classifications are summarized in the table below. x1
10 test points were classified using a 4-nearest neighbors algorithm. The actual y labels and the 4-NN classifications are summarized in the table below.
| x1 | x2 | y+labels | predicted+y |
| 4.8 | 6.2 | 0 | 1 |
| 5 | 1 | 0 | 0 |
| 1.7 | 2 | 1 | 1 |
| 7.6 | 4.3 | 0 | 0 |
| 7 | 5.1 | 1 | 1 |
| 4.2 | 9.8 | 1 | 1 |
| 2.2 | 9 | 1 | 1 |
| 4.6 | 8 | 1 | 1 |
| 9.7 | 6.4 | 0 | 0 |
| 9.2 | 5.9 | 0 | 1 |
a. Using the above information, need to fill out the confusion matrix below.
| Predicted Value | |||
| 0 | 1 | ||
| Actual | 0 | ||
| Value | 1 |
b. What is the overall error rate for this 4-nearest neighbours algorithm?
Error Rate (%) = ?
c. What is the accuracy for this 4-nearest neighbours algorithm?
Accuracy (%) = ?
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