nnet::multinom Multinomial Learner

nnet::multinom Multinomial Learner

Usage

lnr_multinomial_nnet(data, formula, ...)

Arguments

data

A dataframe to train a learner / learners on.

formula

A regression formula to use inside this learner.

...

Any extra arguments that should be passed to the internal model for model fitting purposes.

Examples

df <- mtcars
df$cyl <- as.factor(df$cyl)
lnr_multinomial_nnet(df, cyl ~ hp + mpg)(df)
#>  [1] 1.0000000 1.0000000 1.0000000 0.9996982 1.0000000 1.0000000 1.0000000
#>  [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> [22] 1.0000000 1.0000000 1.0000000 0.9998992 1.0000000 1.0000000 1.0000000
#> [29] 1.0000000 0.9999127 1.0000000 0.9997039
lnr_multinomial_nnet(iris, Species ~ .)(iris)
#>   [1] 1.0000000 0.9999996 1.0000000 0.9999968 1.0000000 1.0000000 1.0000000
#>   [8] 1.0000000 0.9999871 0.9999992 1.0000000 0.9999997 0.9999992 0.9999998
#>  [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9999999
#>  [22] 1.0000000 1.0000000 0.9999998 0.9999768 0.9999965 0.9999999 1.0000000
#>  [29] 1.0000000 0.9999968 0.9999956 1.0000000 1.0000000 1.0000000 0.9999994
#>  [36] 1.0000000 1.0000000 1.0000000 0.9999987 1.0000000 1.0000000 0.9997542
#>  [43] 0.9999998 1.0000000 0.9999999 0.9999996 1.0000000 0.9999997 1.0000000
#>  [50] 1.0000000 0.9999877 0.9999501 0.9987828 0.9999567 0.9985711 0.9998954
#>  [57] 0.9986727 0.9999997 0.9999850 0.9999848 1.0000000 0.9999615 0.9999999
#>  [64] 0.9991850 0.9999600 0.9999957 0.9986481 1.0000000 0.9401019 0.9999999
#>  [71] 0.5945365 0.9999988 0.7743208 0.9999586 0.9999984 0.9999924 0.9992755
#>  [78] 0.7236305 0.9990177 0.9999917 0.9999999 1.0000000 0.9999997 0.1323524
#>  [85] 0.9977885 0.9997823 0.9996965 0.9997399 0.9999991 0.9999886 0.9999591
#>  [92] 0.9998366 0.9999995 0.9999998 0.9999845 0.9999997 0.9999968 0.9999976
#>  [99] 0.9997776 0.9999976 1.0000000 0.9996078 0.9999990 0.9997148 0.9999999
#> [106] 1.0000000 0.8908074 0.9999954 0.9999919 1.0000000 0.9901387 0.9997381
#> [113] 0.9999794 0.9999665 0.9999999 0.9999950 0.9976741 0.9999999 1.0000000
#> [120] 0.9203566 0.9999996 0.9995049 1.0000000 0.9480610 0.9999819 0.9995521
#> [127] 0.8239052 0.8019269 0.9999992 0.9710712 0.9999968 0.9999172 0.9999999
#> [134] 0.2060534 0.9664645 1.0000000 0.9999999 0.9964650 0.6689415 0.9998686
#> [141] 0.9999999 0.9999423 0.9996078 1.0000000 1.0000000 0.9999929 0.9990906
#> [148] 0.9989764 0.9999955 0.9775646