## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE  # examples shown but not run during check (require optional deps)
)

## ----setup--------------------------------------------------------------------
# library(ml)

## ----profile------------------------------------------------------------------
# prof <- ml_profile(iris, "Species")
# prof

## ----split--------------------------------------------------------------------
# s <- ml_split(iris, "Species", seed = 42)
# s

## ----screen-------------------------------------------------------------------
# lb <- ml_screen(s, "Species", seed = 42)
# lb

## ----fit-evaluate-------------------------------------------------------------
# model <- ml_fit(s$train, "Species", algorithm = "logistic", seed = 42)
# model
# 
# metrics <- ml_evaluate(model, s$valid)
# metrics

## ----explain------------------------------------------------------------------
# exp <- ml_explain(model)
# exp

## ----validate-----------------------------------------------------------------
# gate <- ml_validate(model,
#                     test  = s$test,
#                     rules = list(accuracy = ">0.70"))
# gate

## ----assess-------------------------------------------------------------------
# verdict <- ml_assess(model, test = s$test)
# verdict

## ----io, eval = FALSE---------------------------------------------------------
# path <- file.path(tempdir(), "iris_model.mlr")
# ml_save(model, path)
# loaded <- ml_load(path)
# predict(loaded, s$valid)[1:5]

## ----module-style-------------------------------------------------------------
# # Identical results — pick the style you prefer
# m2 <- ml$fit(s$train, "Species", algorithm = "logistic", seed = 42)
# identical(predict(model, s$valid), predict(m2, s$valid))

## ----regression---------------------------------------------------------------
# s2   <- ml_split(mtcars, "mpg", seed = 42)
# m_rf <- ml_fit(s2$train, "mpg", seed = 42)
# ml_evaluate(m_rf, s2$valid)

## ----algorithms---------------------------------------------------------------
# ml_algorithms()