Plot method for class “evaluation_log”

Draws line plots of training and test errors in an evaluation log, if available.

Usage

# S3 method for evaluation_log
plot(
  x,
  errorbars = getOption("cv_show_se"),
  plot = TRUE,
  size = 2,
  lwd = 1,
  lwd_errorbars = 0.5,
  zeroline = TRUE,
  ...
)

Arguments

x

An object of class evaluation_log.

errorbars

Logical: Whether to add error bars in plots.

plot

Logical: If TRUE, a ggplot is returned, if FALSE a data.frame. plot() first prepares a data.frame and then draws some ggplot using this data, with limited options for customization. If you want to design your own plot, you can set plot=FALSE, and use the data.frame returned by plot() to create your plot.

size

Graphic detail: Size of point.

lwd

Graphic detail: Line width of interpolating line.

lwd_errorbars

Graphic detail: Line width of errorbars.

zeroline

Logical: Whether to include a horizontal reference line at level 0.

...

Currently not used.

Value

plot.evaluation_log() returns either a ggplot or, if plot=FALSE, a data.frame.

Details

The lengths of the errorbars corresponds to +/-1 standard error.

See also

evaluation_log, ifm

Examples

# Evaluation log of a 'fm_xgb' model
fitted_xgb <- fm_xgb(Sepal.Length ~ ., iris, max_depth = 2)
evaluation_log(fitted_xgb)    # evaluation log of a model has no 
#> ‘evaluation_log’, 1 model:
#> 
#> Model ‘fitted_xgb’:
#>   model class: fm_xgb
#>  iter train_rmse test_rmse
#>     1      3.823        NA
#>    21      0.258        NA
#>    41      0.227        NA
#>    60      0.209        NA
#>    80      0.197        NA
#>   100      0.188        NA
plot(evaluation_log(fitted_xgb))
#> Warning: Removed 100 rows containing missing values (`geom_point()`).
#> Warning: Removed 100 rows containing missing values (`geom_line()`).


# Evaluation log of cross-validated 'fm_xgb' model
cv_xgb <- cv(model(fitted_xgb, label = "xgb_depth2"))
evaluation_log(cv_xgb) 
#> ‘evaluation_log’, 1 cross-validated model:
#> 
#> Model ‘xgb_depth2’:
#>   model class: fm_xgb
#>  iter train_rmse test_rmse criterion
#>     1      3.825     3.821          
#>     8      0.472     0.500          
#>    14      0.280     0.337          
#>    21      0.250     0.325          
#>    24      0.244     0.322       min
#>    27      0.238     0.323          
#>    34      0.227     0.325          
plot(evaluation_log(cv_xgb))


# Evaluation log of several cross-validated models
mydata <- simuldat()
fitted_glmnet <- fm_glmnet(Y ~ ., mydata)
cv_glmnet <- cv(multimodel(fitted_glmnet, prefix = "glmnet", alpha = 0:1))
label(cv_glmnet) <- c("ridge", "lasso")
evaluation_log(cv_glmnet)
#> ‘evaluation_log’, 2 cross-validated models:
#> 
#> Model ‘ridge’:
#>   model class: fm_glmnet
#>  iter   lambda train_rmse test_rmse criterion
#>     1 1039.726       2.76      2.75          
#>    21  161.748       2.74      2.73          
#>    41   25.163       2.62      2.62          
#>    60    4.296       2.25      2.30          
#>    80    0.668       1.93      2.04          
#>    92    0.219       1.90      2.03       min
#>   100    0.104       1.89      2.03          
#> 
#> Model ‘lasso’:
#>   model class: fm_glmnet
#>  iter  lambda train_rmse test_rmse criterion
#>     1 1.03973       2.76      2.76          
#>    14 0.31022       2.18      2.20          
#>    27 0.09256       1.95      2.05          
#>    40 0.02762       1.90      2.03          
#>    44 0.01903       1.90      2.03       min
#>    53 0.00824       1.89      2.03          
#>    66 0.00246       1.89      2.03          
plot(evaluation_log(cv_glmnet))