Get cutoff values for group predictions

get_cutoffs(x)

Arguments

x	Data frame from `predict.model_list` where `outcome_groups` or `risk_groups` was specified

Value

A message is printed about the thresholds. If outcome_groups were defined the return value is a single numeric value, the threshold used to separate predicted probabilities into outcome groups. If risk_groups were defined the return value is a data frame with one column giving the group names and another column giving the minimum predicted probability for an observation to be in that group.

Examples

machine_learn(pima_diabetes[1:20, ], patient_id, outcome = diabetes,
              models = "xgb", tune = FALSE) %>%
  predict(risk_groups = 5) %>%
  get_cutoffs()
#> 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 20 observations.
#> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 5 xgb's 
#> Training at fixed values: eXtreme Gradient Boosting
#> 
#> *** 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. ***
#> Risk groups defined by the following thresholds:
#> # A tibble: 5 x 2
#>   group       minimum_probability
#>   <chr>                     <dbl>
#> 1 risk_group1               0.908
#> 2 risk_group2               0.797
#> 3 risk_group3               0.607
#> 4 risk_group4               0.521
#> 5 risk_group5               0