mplot_topcats: Top Hit Ratios for Multi-Classification Models

In lares: Lean Analytics and Robust Exploration Sidekick

Top Hit Ratios for Multi-Classification Models

Description

Calculate and plot a multi-class model's predictions accuracy based on top N predictions and distribution of probabilities.

Usage

mplot_topcats(tag, score, multis, model_name = NA)

Arguments

tag	Vector. Real known label.
score	Vector. Predicted value or model's result.
multis	Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
model_name	Character. Model's name

Value

Plot with performance results over most frequent categories.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
mplot_topcats(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  model_name = "Titanic Class Model"
)

lares documentation built on Aug. 18, 2025, 5:12 p.m.