Question: 2 . Here is a dataset, using Euclidian Distance ( in handout 4 slide 1 0 ) Measure and the 5 - nearest neighbor techniques,

2. Here is a dataset, using Euclidian Distance (in handout4 slide 10) Measure and the 5-nearest neighbor techniques, predict the following new data is likely to be in which class based on
COSC6337
Web Mining and Information Retrieval
simple majority voting. (Hint, combine the weight and height attributes into a ratio=weight/height, which could simply the 2 dimensions data into 1 dimension). If you want to use the \(2-\mathrm{d}\) vector to calculate, you need to normalize first (since weight is in the range of [130,289] and height is in the range of [1.5,2.4], which are not the same measurement).
\begin{tabular}{|l|l|l|l|}
\hline Name & Weight & Height & Class \\
\hline Kristina & 160 lb & 1.6 m & Average \\
\hline Jim & 210 lb & 2.0 m & Average \\
\hline Maggie & 207 lb & 1.9 m & Average \\
\hline Martha & 130 lb & 1.8 m & Underweight \\
\hline Stephanie & 221 lb & 1.7 m & Overweight \\
\hline Bob & 215 lb & 1.8 m & Average \\
\hline Kathy & 178 lb & 1.6 m & Average \\
\hline Dave & 138 lb & 1.7 m & Underweight \\
\hline Worth & 160 lb & 2.2 m & Underweight \\
\hline Steven & 190 lb & 2.1 m & Average \\
\hline Debbie & 234 lb & 1.8 m & Overweight \\
\hline Todd & 285 lb & 1.9 m & Overweight \\
\hline Kim & 135 lb & 1.9 m & Underweight \\
\hline Amy & 198 lb & 1.8 m & Average \\
\hline Lynette & 289 lb & 1.7 m & Overweight \\
\hline
\end{tabular}
a) John [185 lb,2.0 m\(]\)
b)\(\quad \) Kelly [\(165\mathrm{lb},1.5\mathrm{~m}]\)
c)\(\quad \) Sam \([180\mathrm{lb},2.4\mathrm{~m}]\)
d) Laura [195 lb,1.8 m ]
e)\(\quad \) Mike \([220\mathrm{lb},1.7\mathrm{~m}]\)
2 . Here is a dataset, using Euclidian Distance (

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