lares (version 4.7)

mplot_roc: ROC Curve Plot

Description

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

Usage

mplot_roc(tag, score, multis = NA, sample = 1000, model_name = NA,
  subtitle = NA, interval = 0.2, plotly = FALSE, save = FALSE,
  subdir = NA, file_name = "viz_roc.png")

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)

sample

Integer. Number of samples to use for rendering plot.

model_name

Character. Model's name

subtitle

Character. Subtitle to show in plot

interval

Numeric. Interval for breaks in plot

plotly

Boolean. Use plotly for plot's output for an interactive plot

save

Boolean. Save output plot into working directory

subdir

Character. Sub directory on which you wish to save the plot

file_name

Character. File name as you wish to save the plot

See Also

Other Machine Learning: ROC, clusterKmeans, 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, model_metrics, mplot_conf, mplot_cuts_error, mplot_cuts, mplot_density, mplot_full, mplot_gain, mplot_importance, mplot_lineal, mplot_metrics, mplot_response, mplot_splits, msplit

Other Visualization: corr_plot, distr, freqs_df, freqs, mplot_conf, mplot_cuts_error, mplot_cuts, mplot_density, mplot_full, mplot_gain, mplot_importance, mplot_lineal, mplot_metrics, mplot_response, mplot_splits, noPlot, plot_survey, theme_lares2, theme_lares, tree_var