run_models: Runs the predictive models for the given datasets.

In jbryer/mldash: Evaluating Machine Learning Models Across Many Datasets

Runs the predictive models for the given datasets.

Description

Runs the predictive models for the given datasets.

Usage

run_models(
  datasets,
  models,
  seed = sample(1:2^15, 1),
  training_size = 0.7,
  print_errors = FALSE,
  metrics = mldash::get_all_metrics(),
  save_model_fits = FALSE,
  timeout = 60
)

Arguments

datasets	the datasets to run the models with. Results from read_ml_datasets().
models	the models to run. Results from read_ml_models().
seed	random seed to set before randomly selecting the training dataset.
training_size	the proportion of the data that should be used for training. The remaining percentage will be used for validation.
print_errors	if TRUE errors will be printed while the function runs. Errors will always be saved in the returned object.
metrics	list of model performance metrics from the yardstick package. See https://yardstick.tidymodels.org/articles/metric-types.html for more information.
timeout	the maximum amount of time (in seconds) a model is allowed to run before it is interrupted, can be Inf to never expire. Note that not all models can be interrupted. See R.utils::withTimeout() for more information.

Value

a data.frame with the results of all the models run against all the datasets.

jbryer/mldash documentation built on March 4, 2023, 9:35 p.m.