Skip to contents

Find the best learner in terms of RMSE among specified learners using cross validation

Source: R/fit_best_learner.R
fit_best_learner.Rd

Find the best learner in terms of RMSE among specified learners using cross validation

Usage

fit_best_learner(
  preproc,
  data,
  cv_folds = 5,
  learners = default_learners(),
  verbose = options::opt("verbose")
)

Arguments

preproc

A list (preferably named) with preprocessing objects: formulas, recipes, or workflows::workflow_variables(). Passed to workflowsets::workflow_set().

data

A data frame.

cv_folds

a numeric with the number of cross-validation folds used when fitting and evaluating models

learners

a list (preferably named) containing named lists of elements model and optionally grid. The model element should be a parsnip model specification, which is passed to workflowsets::workflow_set as the model argument, while the grid element is passed as the grid argument of workflowsets::option_add

verbose

numeric verbosity level. Higher values means more information is printed in console. A value of 0 means nothing is printed to console during execution (Defaults to 2, overwritable using option 'postcard.verbose' or environment variable 'R_POSTCARD_VERBOSE')

Value

a trained workflow

Details

Ensure data compatibility with the learners.

See also

See rctglm_with_prognosticscore() for a function that utilises this function to perform prognostic covariate adjustment.

Examples

# Generate some synthetic 2-armed RCT data along with historical controls
n <- 100
dat_rct <- glm_data(
  Y ~ 1+2*x1+3*a,
  x1 = rnorm(n, 2),
  a = rbinom (n, 1, .5),
  family = gaussian()
)
dat_hist <- glm_data(
  Y ~ 1+2*x1,
  x1 = rnorm(n, 2),
  family = gaussian()
)

# Fit a learner to the historical control data
learners <- list(
  mars = list(
    model = parsnip::set_engine(
      parsnip::mars(
        mode = "regression", prod_degree = 3
      ),
      "earth"
    )
  )
)
fit <- fit_best_learner(
  preproc = list(mod = Y ~ .),
  data = dat_hist,
  learners = learners
)
#> ℹ Fitting learners
#> • mod_mars
#> i	No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 1 resampling: mod_mars
#> ✔ 1 of 1 resampling: mod_mars (183ms)
#> ℹ Model with lowest RMSE: mod_mars

# Use it fx. to predict the "control outcome" in the 2-armed RCT
predict(fit, new_data = dat_rct)
#> # A tibble: 100 × 1
#>     .pred
#>     <dbl>
#>  1  2.17 
#>  2  5.54 
#>  3 -0.219
#>  4  5.04 
#>  5  6.23 
#>  6  7.22 
#>  7  1.20 
#>  8  4.59 
#>  9  4.59 
#> 10  4.52 
#> # ℹ 90 more rows