mplot_importance: Variables Importances Plot

In lares: Analytics & Machine Learning Sidekick

Variables Importances Plot

Description

This function plots Variable Importances

Usage

mplot_importance(
  var,
  imp,
  colours = NA,
  limit = 15,
  model_name = NA,
  subtitle = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_importance.png"
)

Arguments

var	Vector. Variable or column's names
imp	Vector. Importance of said variables. Must have same length as var
colours	If positive and negative contribution is known
limit	Integer. Limit how many variables you wish to plot
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 ranked importance variables results.

See Also

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

Examples

Sys.unsetenv("LARES_FONT") # Temporal
df <- data.frame(
  variable = LETTERS[1:6],
  importance = c(4, 6, 6.7, 3, 4.8, 6.2) / 100,
  positive = c(TRUE, TRUE, FALSE, TRUE, FALSE, FALSE)
)
head(df)

mplot_importance(
  var = df$variable,
  imp = df$importance,
  model_name = "Random values model"
)

# Add a colour for categories
mplot_importance(
  var = df$variable,
  imp = df$importance,
  colours = df$positive,
  limit = 4
)

lares documentation built on Nov. 5, 2023, 1:09 a.m.