lares (version 5.2.13)

ROC: AUC and ROC Curves Data

Description

This function calculates ROC Curves and AUC values with 95% confidence range. It also works for multi-categorical models.

Usage

ROC(tag, score, multis = NA)

Value

List with ROC's results, area under the curve (AUC) and their CI.

Arguments

tag

Vector. Real known label

score

Vector. Predicted value or model's result

multis

Data.frame. Containing columns with each category score (only used when more than 2 categories coexist)

Plot Results

To plot results, use the mplot_roc() function.

See Also

Other Machine Learning: conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Model metrics: conf_mat(), errors(), gain_lift(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 2)], head)

# ROC Data for Binomial Model
roc1 <- ROC(dfr$class2$tag, dfr$class2$scores)
lapply(roc1, head)

# ROC Data for Multi-Categorical Model
roc2 <- ROC(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score))
)
lapply(roc2, head)