mikropml (version 1.6.1)

train_model: Train model using caret::train().

Description

Train model using caret::train().

Usage

train_model(
  train_data,
  outcome_colname,
  method,
  cv,
  perf_metric_name,
  tune_grid,
  ...
)

Value

Trained model from caret::train().

Arguments

train_data

Training data. Expected to be a subset of the full dataset.

outcome_colname

Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).

method

ML method. Options: c("glmnet", "rf", "rpart2", "svmRadial", "xgbTree").

• glmnet: linear, logistic, or multiclass regression

• rf: random forest

• rpart2: decision tree

• svmRadial: support vector machine

• xgbTree: xgboost

cv

Cross-validation caret scheme from define_cv().

perf_metric_name

The column name from the output of the function provided to perf_metric_function that is to be used as the performance metric. Defaults: binary classification = "ROC", multi-class classification = "logLoss", regression = "RMSE".

tune_grid

Tuning grid from get_tuning_grid().#'

...

All additional arguments are passed on to caret::train(), such as case weights via the weights argument or ntree for rf models. See the caret::train() docs for more details.

Author

Zena Lapp,

Examples

if (FALSE) {
training_data <- otu_mini_bin_results_glmnet$trained_model$trainingData %>%
  dplyr::rename(dx = .outcome)
method <- "rf"
hyperparameters <- get_hyperparams_list(otu_mini_bin, method)
cross_val <- define_cv(training_data,
  "dx",
  hyperparameters,
  perf_metric_function = caret::multiClassSummary,
  class_probs = TRUE,
  cv_times = 2
)
tune_grid <- get_tuning_grid(hyperparameters, method)

rf_model <- train_model(
  training_data,
  "dx",
  method,
  cross_val,
  "AUC",
  tune_grid,
  ntree = 1000
)
rf_model$results %>% dplyr::select(mtry, AUC, prAUC)
}