calc_perf_metrics: Get performance metrics for test data
In mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

calc_perf_metrics

R Documentation

Get performance metrics for test data

Description

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(s)

Zena Lapp, zenalapp@umich.edu

Examples

## Not run: 
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
)

## End(Not run)

mikropml documentation built on Aug. 21, 2023, 5:10 p.m.