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.