Hantera predikatorer med låg informationshalt

Maskininlärning med caret i R

Zach Mayer

Data Scientist at DataRobot and co-author of caret

Variabler utan (eller med låg) varians

Vissa variabler innehåller lite information
- Konstanta (dvs. ingen varians)
- Nästan konstanta (dvs. låg varians)
En fold i CV kan lätt få en konstant kolumn
- Det kan orsaka problem i dina modeller
Ta vanligtvis bort variabler med extremt låg varians

Exempel: konstant kolumn i mtcars

# Reproduce dataset from last video
data(mtcars)
set.seed(42)
mtcars[sample(1:nrow(mtcars), 10), "hp"] <- NA
Y <- mtcars$mpg
X <- mtcars[, 2:4]

# Add constant-valued column to mtcars
X$bad <- 1

Exempel: konstant kolumn i mtcars

# Try to fit a model with PCA + glm
model <- train(
  X, Y, method = "glm", 
  preProcess = c("center", "scale", "medianImpute", "pca"))

Warning in preProcess.default(thresh = 0.95, k = 5, method = c("medianImpute",  :
  These variables have zero variances: bad
Something is wrong; all the RMSE metric values are missing:
      RMSE        Rsquared  
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :1     NA's   :1

caret räddar situationen (igen)

"zv" tar bort konstanta kolumner
"nzv" tar bort nästan konstanta kolumner

# Have caret remove those columns during modeling
set.seed(42)
model <- train(
  X, Y, method = "glm", 
  preProcess = c("zv", "center", "scale", "medianImpute", "pca")
)
min(model$results$RMSE)

3.402557

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Maskininlärning med caret i R