Get performance metrics for test data

Usage

calc_perf_metrics(
  test_data,
  trained_model,
  outcome_colname,
  perf_metric_function,
  class_probs
)

Arguments

test_data: Held out test data: dataframe of outcome and features.
trained_model: Trained model from caret::train().
outcome_colname: Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).
perf_metric_function: Function to calculate the performance metric to be used for cross-validation and test performance. Some functions are provided by caret (see caret::defaultSummary()). Defaults: binary classification = twoClassSummary, multi-class classification = multiClassSummary, regression = defaultSummary.
class_probs: Whether to use class probabilities (TRUE for categorical outcomes, FALSE for numeric outcomes).

Value

Dataframe of performance metrics.

Author

Zena Lapp, zenalapp@umich.edu

Examples

if (FALSE) {
results <- run_ml(otu_small, "glmnet", kfold = 2, cv_times = 2)
calc_perf_metrics(results$test_data,
  results$trained_model,
  "dx",
  multiClassSummary,
  class_probs = TRUE
)
}