flash_models: Train models without tuning for performance

In healthcareai: Tools for Healthcare Machine Learning

Train models without tuning for performance

Description

Train models without tuning for performance

Usage

flash_models(
  d,
  outcome,
  models,
  metric,
  positive_class,
  n_folds = 5,
  model_class,
  model_name = NULL,
  allow_parallel = FALSE
)

Arguments

d	A data frame from prep_data. If you want to prepare your data on your own, use prep_data(..., no_prep = TRUE).
outcome	Optional. Name of the column to predict. When omitted the outcome from prep_data is used; otherwise it must match the outcome provided to prep_data.
models	Names of models to try. See get_supported_models for available models. Default is all available models.
metric	Which metric should be used to assess model performance? Options for classification: "ROC" (default) (area under the receiver operating characteristic curve) or "PR" (area under the precision-recall curve). Options for regression: "RMSE" (default) (root-mean-squared error, default), "MAE" (mean-absolute error), or "Rsquared." Options for multiclass: "Accuracy" (default) or "Kappa" (accuracy, adjusted for class imbalance).
positive_class	For classification only, which outcome level is the "yes" case, i.e. should be associated with high probabilities? Defaults to "Y" or "yes" if present, otherwise is the first level of the outcome variable (first alphabetically if the training data outcome was not already a factor).
n_folds	How many folds to train the model on. Default = 5, minimum = 2. Whie flash_models doesn't use cross validation to tune hyperparameters, it trains n_folds models to evaluate performance out of fold.
model_class	"regression" or "classification". If not provided, this will be determined by the class of 'outcome' with the determination displayed in a message.
model_name	Quoted, name of the model. Defaults to the name of the outcome variable.
allow_parallel	Depreciated. Instead, control the number of cores though your parallel back end (e.g. with doMC).

Details

This function has two major differences from tune_models: 1. It uses fixed default hyperparameter values to train models instead of using cross-validation to optimize hyperparameter values for predictive performance, and, as a result, 2. It is much faster.

If you want to train a model at a single set of non-default hyperparameter values use tune_models and pass a single-row data frame to the hyperparameters arguemet.

Value

A model_list object. You can call plot, summary, evaluate, or predict on a model_list.

See Also

For setting up model training: prep_data, supported_models, hyperparameters

For evaluating models: plot.model_list, evaluate.model_list

For making predictions: predict.model_list

For optimizing performance: tune_models

To prepare data and tune models in a single step: machine_learn

Examples

# Prepare data
prepped_data <- prep_data(pima_diabetes, patient_id, outcome = diabetes)

# Get models quickly at default hyperparameter values
flash_models(prepped_data)

# Speed comparison of no tuning with flash_models vs. tuning with tune_models:
# ~15 seconds:
system.time(
  tune_models(prepped_data, diabetes)
)
# ~3 seconds:
system.time(
  flash_models(prepped_data, diabetes)
)

healthcareai documentation built on Sept. 5, 2022, 5:12 p.m.