get_performance_tbl: Get model performance metrics as a one-row tibble

In mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

Get model performance metrics as a one-row tibble

Description

Get model performance metrics as a one-row tibble

Usage

get_performance_tbl(
  trained_model,
  test_data,
  outcome_colname,
  perf_metric_function,
  perf_metric_name,
  class_probs,
  method,
  seed = NA
)

Arguments

trained_model	Trained model from caret::train().
test_data	Held out test data: dataframe of outcome and features.
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.
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".
class_probs	Whether to use class probabilities (TRUE for categorical outcomes, FALSE for numeric outcomes).
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
seed	Random seed (default: NA). Your results will only be reproducible if you set a seed.

Value

A one-row tibble with a column for the cross-validation performance, columns for each of the performance metrics for the test data, plus the method, and seed.

Author(s)

Kelly Sovacool, [e-mail]

Zena Lapp, [e-mail]

Examples

## Not run: 
results <- run_ml(otu_small, "glmnet", kfold = 2, cv_times = 2)
names(results$trained_model$trainingData)[1] <- "dx"
get_performance_tbl(results$trained_model, results$test_data,
  "dx",
  multiClassSummary, "AUC",
  class_probs = TRUE,
  method = "glmnet"
)

## End(Not run)

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