performance_curve: Model Performance Curves

In MachineShop: Machine Learning Models and Tools

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, ...)

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

## S3 method for class 'Resample'
performance_curve(
  x,
  metrics = c(MachineShop::tpr, MachineShop::fpr),
  na.rm = TRUE,
  ...
)

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.

Value

PerformanceCurve class object that inherits from data.frame.

See Also

auc, c, plot, summary

Examples

## 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)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.