Create a modeling pipeline

Usage

tl_pipeline(
  data,
  formula,
  preprocessing = NULL,
  models = NULL,
  evaluation = NULL,
  ...
)

Arguments

data: A data frame containing the data
formula: A formula specifying the model
preprocessing: A list of preprocessing steps
models: A list of models to train
evaluation: A list of evaluation criteria
...: Additional arguments

Value

A tidylearn_pipeline object (S3 list) with components $formula, $data, $preprocessing, $models, $evaluation, and $results (initially NULL; populated after tl_run_pipeline).

Examples

# \donttest{
pipe <- tl_pipeline(iris, Species ~ .,
  models = list(tree = list(method = "tree")))
print(pipe)
#> Tidylearn Pipeline
#> =================
#> Formula: Species ~ . 
#> Data: 150 observations, 5 variables
#> Preprocessing: impute_missing, standardize, dummy_encode 
#> Models: tree 
#> Evaluation:  cv (5 folds)
#> Metrics: accuracy, precision, recall, f1, auc 
#> Best metric: f1 
# }