Training data for a 2-nearest neighbours algorithm is shown below. x1 x2 y+labels Standardizedx1 Standardizedx2 y Label
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| Training data for a 2-nearest neighbours algorithm is shown below. | |||||||||||
| x1 | x2 | y+labels | Standardizedx1 | Standardizedx2 | y Label | ||||||
| 6.5 | 133.4 | 0 | |||||||||
| 9.8 | 243.4 | 1 | |||||||||
| 3.4 | 284.5 | 0 | |||||||||
| 2.4 | 137.4 | 1 | |||||||||
| 1.5 | 127.1 | 1 | |||||||||
| 2.8 | 278.7 | 1 | |||||||||
| 8.4 | 133.7 | 0 | |||||||||
| 2.2 | 165.2 | 0 | |||||||||
| 4.8 | 231.9 | 0 | |||||||||
| 2.1 | 116.1 | 1 | |||||||||
| Mean | 4.39 | 185.14 | |||||||||
| Std | 2.75 | 63.52 | |||||||||
| With the X1 and X2 variables having drastically different scales, you must standardize the data before performing the 2-nearest neighbours algorithm. Use the z-score standardization method to standardize the X1 and X2 variables. Round the standardized values to two decimal places. Enter the revised training data into the table below. Do not change the order of the data. | |||||||||||
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