When predicting from a model, it is often important for the new_data to
have the same classes as the original data used to fit the model.
get_data_classes() extracts the classes from the original training data.
Value
A named list. The names are the column names of data and the values are
character vectors containing the class of that column.
Examples
get_data_classes(iris)
#> $Sepal.Length
#> [1] "numeric"
#>
#> $Sepal.Width
#> [1] "numeric"
#>
#> $Petal.Length
#> [1] "numeric"
#>
#> $Petal.Width
#> [1] "numeric"
#>
#> $Species
#> [1] "factor"
#>
get_data_classes(as.matrix(mtcars))
#> $mpg
#> [1] "numeric"
#>
#> $cyl
#> [1] "numeric"
#>
#> $disp
#> [1] "numeric"
#>
#> $hp
#> [1] "numeric"
#>
#> $drat
#> [1] "numeric"
#>
#> $wt
#> [1] "numeric"
#>
#> $qsec
#> [1] "numeric"
#>
#> $vs
#> [1] "numeric"
#>
#> $am
#> [1] "numeric"
#>
#> $gear
#> [1] "numeric"
#>
#> $carb
#> [1] "numeric"
#>
# Unlike .MFclass(), the full class
# vector is returned
data <- data.frame(col = ordered(c("a", "b")))
.MFclass(data$col)
#> [1] "ordered"
get_data_classes(data)
#> $col
#> [1] "ordered" "factor"
#>