Top Hit Ratios for Multi-Classification Models

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"
)