Plot performance of models

# S3 method for model_list
plot(x, font_size = 11, point_size = 1, print = TRUE,
  ...)

Arguments

x

modellist object as returned by tune_models or machine_learn

font_size

Relative size of all fonts in plot, default = 11

point_size

Size of dots, default = 3

print

If TRUE (default) plot is printed

...

Unused

Value

Plot of model performance as a function of algorithm and hyperparameter values tuned over. Generally called for the side effect of printing a plot, but the plot is also invisibly returned. The best-performing model within each algorithm will be plotted as a triangle.

Examples

models <- machine_learn(mtcars, outcome = mpg, models = "glm")
#> Training new data prep recipe...
#> #> mpg looks numeric, so training regression algorithms.
#> #> After data processing, models are being trained on 10 features with 32 observations. #> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 100 glm's
#> Training with cross validation: glmnet
#> #> *** Models successfully trained. The model object contains the training data minus ignored ID columns. *** #> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
plot(models)