1.2 weka arff数据文件的解释
supervised learning:监督学习
用weka做分类预测
数据 ：weather.numeric.arff temperature和huidity是数字，而不是描述性文字。
解释
会自动统计好最大值、最小值、平均值和标准差。
weka中的数据格式
@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature numeric
@attribute humidity numeric
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
@data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71,91,TRUE,no
%代表注释，@代表属性解释，数字用numeric,每个属性具体的解释用{}括起来。具体的data之间用","进行分隔。
比如glass样例：
@relation Glass
@attribute 'RI' numeric
@attribute 'Na' numeric
@attribute 'Mg' numeric
@attribute 'Al' numeric
@attribute 'Si' numeric
@attribute 'K' numeric
@attribute 'Ca' numeric
@attribute 'Ba' numeric
@attribute 'Fe' numeric
@attribute 'Type' { 'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@data
1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'
1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float'
1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0,0,'build wind float'
1.51299,14.4,1.74,1.54,74.55,0,7.59,0,0,tableware