Get model performance metrics

evaluate(x, ...)

# S3 method for predicted_df
evaluate(x, na.rm = FALSE, ...)

# S3 method for model_list
evaluate(x, all_models = FALSE, ...)



Object to be evaluted


Not used


Logical. If FALSE (default) performance metrics will be NA if any rows are missing an outcome value. If TRUE, performance will be evaluted on the rows that have an outcome value. Only used when evaluating a prediction data frame.


Logical. If FALSE (default), a numeric vector giving performance metrics for the best-performing model is returned. If TRUE, a data frame with performance metrics for all trained models is returned. Only used when evaluating a model_list.


Either a numeric vector or a data frame depending on the value of all_models


This function gets model performance from a model_list object that comes from machine_learn, tune_models, flash_models, or a data frame of predictions from predict.model_list. For the latter, the data passed to predict.model_list must contain observed outcomes. If you have predictions and outcomes in a different format, see evaluate_classification or evaluate_regression instead.

You may notice that evaluate(models) and evaluate(predict(models)) return slightly different performance metrics, even though they are being calculated on the same (out-of-fold) predictions. This is because metrics in training (returned from evaluate(models)) are calculated within each cross-validation fold and then averaged, while metrics calculated on the prediction data frame (evaluate(predict(models))) are calculated once on all observations.


models <- machine_learn(pima_diabetes[1:40, ], patient_id, outcome = diabetes, models = c("XGB", "RF"), tune = FALSE, n_folds = 3)
#> Training new data prep recipe...
#> Variable(s) ignored in prep_data won't be used to tune models: patient_id
#> #> diabetes looks categorical, so training classification algorithms.
#> #> After data processing, models are being trained on 12 features with 40 observations. #> Based on n_folds = 3 and hyperparameter settings, the following number of models will be trained: 3 xgb's and 3 rf's
#> Training at fixed values: eXtreme Gradient Boosting
#> Training at fixed values: Random Forest
#> #> *** Models successfully trained. The model object contains the training data minus ignored ID columns. *** #> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
# By default, evaluate returns performance of only the best model evaluate(models)
#> AUPR AUROC #> 0.5856454 0.6636905
# Set all_models = TRUE to see the performance of all trained models evaluate(models, all_models = TRUE)
#> # A tibble: 2 x 3 #> model AUPR AUROC #> <chr> <dbl> <dbl> #> 1 Random Forest 0.586 0.664 #> 2 eXtreme Gradient Boosting 0.558 0.642
# Can also get performance on a test dataset predictions <- predict(models, newdata = pima_diabetes[41:50, ])
#> Prepping data based on provided recipe
#> AUPR AUROC #> 0.4305556 0.9047619