nnet::multinom Multinomial Learner
Arguments
- data
A dataframe to train a learner / learners on.
- formula
A regression formula to use inside this learner.
- weights
Observation weights; see
?lm- ...
Any extra arguments that should be passed to the internal model for model fitting purposes.
Value
A prediction function that accepts newdata,
which returns predictions (a numeric vector of density prediction values at the
outcome value observed in the newdata conditioning on the predictor
variables in newdata).
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