ROC: AUC and ROC Curves Data

In lares: Analytics & Machine Learning Sidekick

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)

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)

Value

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

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_API(), h2o_predict_MOJO(), h2o_predict_binary(), h2o_predict_model(), 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)

lares documentation built on Nov. 5, 2023, 1:09 a.m.