mplot_response: Cumulative Response Plot

In laresbernardo/lares: Analytics & Machine Learning Sidekick

Cumulative Response Plot

Description

The response gains plot helps us answer the question: When we apply the model and select up until ntile X, what is the expected

Usage

mplot_response(
  tag,
  score,
  multis = NA,
  target = "auto",
  splits = 10,
  highlight = "auto",
  caption = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_response.png",
  quiet = FALSE
)

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).
target	Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only works for binary classes
splits	Integer. Numer of quantiles to split the data
highlight	Character or Integer. Which split should be used for the automatic conclussion in the plot? Set to "auto" for best value, "none" to turn off or the number of split.
caption	Character. Caption to show in 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
quiet	Boolean. Do not show message for auto target?

Value

Plot with cumulative response and performance results by cuts.

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_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Plot for Binomial Model
mplot_response(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "TRUE"
)
mplot_response(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "FALSE"
)

# Plot for Multi-Categorical Model
mplot_response(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  caption = "Titanic Class Model"
)

laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.