library(tidyverse) # Data wrangling
library(tidyfit)   # Auto-ML modeling

Multinomial classification is possible in tidyfit using the methods powered by glmnet (LASSO, Ridge, ElasticNet and AdaLASSO). Currently, none of the other methods support multinomial classification.^[I may add support for multinomial classification with mboost in future.] When the response variable contains more than 2 classes, classify automatically uses a multinomial response for the above-mentioned methods.

Here's an example using the built-in iris dataset:

data("iris")

# For reproducibility
set.seed(42)
ix_tst <- sample(1:nrow(iris), round(nrow(iris)*0.2))

data_trn <- iris[-ix_tst,]
data_tst <- iris[ix_tst,]

as_tibble(iris)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # … with 140 more rows

Penalized classification algorithms to predict Species

The code chunk below fits the above mentioned algorithms on the training split, using a 10-fold cross validation to select optimal penalties. We then obtain out-of-sample predictions using predict. Unlike binomial classification, the fit and pred objects contain a class column with separate coefficients and predictions for each class. The predictions sum to one across classes:

fit <- data_trn %>% 
  classify(Species ~ ., 
           m("lasso"), 
           m("ridge"), 
           m("enet"), 
           m("adalasso"),
           ols = m("ridge", lambda = 1e-5), 
           .cv = "vfold")

pred <- fit %>% 
  predict(data_tst)

Note that we can add unregularized least squares estimates by setting lambda = 0.

Next, we can use yardstick to calculate the log loss accuracy metric and compare the performance of the different models:

metrics <- pred %>% 
  group_by(model, class) %>% 
  mutate(row_n = row_number()) %>% 
  spread(class, prediction) %>% 
  group_by(model) %>% 
  yardstick::mn_log_loss(truth, setosa:virginica)

metrics %>% 
  ggplot(aes(model, .estimate)) +
  geom_col(fill = "darkblue") +
  theme_bw() +
  theme(axis.title.x = element_blank())