Target
Data Preparation (16) - Clean (Ensure Target is Categoric)
2014. 12. 6.타겟의 카테고리 여부 확인하기 > target[1] "rain_tomorrow"> ds[[target]] [1] Yes Yes Yes Yes No No No No Yes No No No No No No No Yes No [19] No No No Yes No No No No No No No Yes Yes No Yes No No No [37] Yes No No No No No No No Yes Yes No No Yes Yes Yes Yes No Yes [55] No No Yes No No No No No No No No No No No No No No No [73] Yes No No No Yes No Yes Yes No No No No No No No No No No [91] Yes Yes No No Yes..
Data Preparation (12) - Clean (Remove Missing Target)
2014. 12. 5.아래는 missing target 을 제거하는 작업이다. > target[1] "rain_tomorrow"> ds[target] rain_tomorrow1 Yes2 Yes3 Yes4 Yes5 No6 No7 No8 No9 Yes10 No11 No12 No> is.na(ds[target]) rain_tomorrow [1,] FALSE [2,] FALSE [3,] FALSE [4,] FALSE [5,] FALSE [6,] FALSE [7,] FALSE [8,] FALSE [9,] FALSE [10,] FALSE> sum(is.na(ds[target]))[1] 0 > ds sum(is.na(ds[target]))[1] 0> dim(ds) // 366 observations, 24 variables[1] 366 ..