Tune hyperparameters using random search

Usage

tl_tune_random(
  data,
  formula,
  method,
  param_space,
  n_iter = 10,
  folds = 5,
  metric = NULL,
  maximize = NULL,
  verbose = TRUE,
  seed = NULL,
  ...
)

Arguments

data

A data frame containing the training data

formula

A formula specifying the model

method

The modeling method to tune

param_space

A named list of parameter spaces to sample from. Each element is read by its type and length:

a function: called with no arguments to draw one value
c(min, max, "log"): log-uniform draw between min and max
two whole numbers: integer range, e.g. c(10, 20) draws from 10:20
three or more whole numbers: a discrete set, sampled from as given
two other numbers: uniform draw between them, e.g. c(0.01, 0.1)
character or factor: categorical, sampled from as given

n_iter

Number of random parameter combinations to try

folds

Number of cross-validation folds

metric

Metric to optimize

maximize

Logical; whether to maximize (TRUE) or minimize (FALSE) the metric

verbose

Logical; whether to print progress

seed

Random seed for reproducibility

...

Additional arguments passed to tl_model

Value

A tidylearn model object fitted with the best hyperparameters. Tuning results are stored as an attribute "tuning_results", a list containing param_space, results (data frame of all evaluated iterations), best_params, best_metric, metric, and maximize.

Examples

# \donttest{
model <- tl_tune_random(mtcars, mpg ~ ., method = "tree",
  param_space = list(cp = c(0.01, 0.1), minsplit = c(10, 20)),
  n_iter = 3, folds = 2, verbose = FALSE)
# }