Plot hyperparameter tuning results

Plot hyperparameter tuning results

Usage

tl_plot_tuning_results(
  model,
  top_n = 5,
  param1 = NULL,
  param2 = NULL,
  plot_type = "scatter"
)

Arguments

model

A tidylearn model object with tuning results

top_n

Number of top parameter sets to highlight

param1

First parameter to plot (for 2D grid or scatter plots)

param2

Second parameter to plot (for 2D grid or scatter plots)

plot_type

Type of plot: "scatter", "grid", "parallel", "importance"

Value

A ggplot object.

Details

A parameter whose candidates are not single values, such as hidden_layers = list(c(10), c(20, 10)) or a parms list, is drawn as a categorical one, each value labelled as the verbose messages print it. The importance of a numeric parameter is the absolute correlation of its values with the score; that of a categorical one is eta squared from a one-way ANOVA of the score, and 0 when the sets that were scored all share one value.

Examples

# \donttest{
model <- tl_tune_grid(iris, Species ~ ., method = "tree",
  param_grid = list(cp = c(0.01, 0.1), minsplit = c(10, 20)),
  folds = 2, verbose = FALSE)
tl_plot_tuning_results(model)

# }