MachineShop (version 3.5.0)

performance_curve: Model Performance Curves

Description

Calculate curves for the analysis of tradeoffs between metrics for assessing performance in classifying binary outcomes over the range of possible cutoff probabilities. Available curves include receiver operating characteristic (ROC) and precision recall.

Usage

performance_curve(x, ...)
# S3 method for default
performance_curve(
  x,
  y,
  weights = NULL,
  metrics = c(MachineShop::tpr, MachineShop::fpr),
  na.rm = TRUE,
  ...
)
# S3 method for Resample
performance_curve(
  x,
  metrics = c(MachineShop::tpr, MachineShop::fpr),
  na.rm = TRUE,
  ...
)

Value

PerformanceCurve class object that inherits from data.frame.

Arguments

x

observed responses or resample result containing observed and predicted responses.

...

arguments passed to other methods.

y

predicted responses if not contained in x.

weights

numeric vector of non-negative case weights for the observed x responses [default: equal weights].

metrics

list of two performance metrics for the analysis [default: ROC metrics]. Precision recall curves can be obtained with c(precision, recall).

na.rm

logical indicating whether to remove observed or predicted responses that are NA when calculating metrics.

See Also

auc, c, plot, summary

Examples

# \donttest{
## Requires prior installation of suggested package gbm to run

data(Pima.tr, package = "MASS")

res <- resample(type ~ ., data = Pima.tr, model = GBMModel)

## ROC curve
roc <- performance_curve(res)
plot(roc)
auc(roc)
# }