## ----include = FALSE----------------------------------------------------------
use_saved_results <- TRUE

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = TRUE,
  eval = !use_saved_results,
  message = FALSE,
  warning = FALSE
)

if (use_saved_results) {
  results <- readRDS("vignette_mc.rds")
  pred <- results$pred
}

## ----eval=TRUE----------------------------------------------------------------
library(dplyr); library(tidyr); library(purrr) # Data wrangling
library(ggplot2); library(stringr) # Plotting
library(tidyfit)   # Auto-ML modeling

## ----eval=TRUE----------------------------------------------------------------
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)

## -----------------------------------------------------------------------------
# fit <- data_trn |>
#   classify(Species ~ .,
#            LASSO = m("lasso"),
#            Ridge = m("ridge"),
#            ElasticNet = m("enet"),
#            AdaLASSO = m("adalasso"),
#            SVM = m("svm"),
#            `Random Forest` = m("rf"),
#            `Least Squares` = m("ridge", lambda = 1e-5),
#            .cv = "vfold_cv")
# 
# pred <- fit |>
#   predict(data_tst)

## ----fig.width=7, fig.height=3, fig.align="center", eval=TRUE-----------------
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 |>
  mutate(model = str_wrap(model, 11)) |>
  ggplot(aes(model, .estimate)) +
  geom_col(fill = "darkblue") +
  theme_bw() +
  theme(axis.title.x = element_blank())