mplot_density: Density plot for discrete and continuous values

In lares: Analytics & Machine Learning Sidekick

Density plot for discrete and continuous values

Description

This function plots discrete and continuous values results

Usage

mplot_density(
  tag,
  score,
  thresh = 6,
  model_name = NA,
  subtitle = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_distribution.png"
)

Arguments

tag	Vector. Real known label
score	Vector. Predicted value or model's result
thresh	Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
model_name	Character. Model's name
subtitle	Character. Subtitle 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

Value

Plot with distribution and performance results.

See Also

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

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 3)], head)

# Plot for binomial results
mplot_density(dfr$class2$tag, dfr$class2$scores, subtitle = "Titanic Survived Model")

# Plot for regression results
mplot_density(dfr$regr$tag, dfr$regr$score, model_name = "Titanic Fare Model")

lares documentation built on Sept. 13, 2024, 1:08 a.m.