Save elements from forester

save(
  train,
  list = "all",
  name = NULL,
  path = NULL,
  verbose = TRUE,
  return_name = FALSE
)

Arguments

train: The return from `train` function.
list: The list of names of elements from train. By default `all` save every element.
name: A name of the file. By default `forester_timestamp`.
path: A path to save the file. By default current working directory.
verbose: A logical value, if set to TRUE, provides all information about training process, if FALSE gives none.
return_name: A logical value, if set to TRUE, function returns \ full path and name of the saved file.

Examples

train <- train(iris[1:100, ], 'Sepal.Width')
#> ✔ Type guessed as:  regression 
#> 
#>  -------------------- CHECK DATA REPORT -------------------- 
#>  
#> The dataset has 100 observations and 5 columns, which names are: 
#> Sepal.Length; Sepal.Width; Petal.Length; Petal.Width; Species; 
#> 
#> With the target value described by a column Sepal.Width.
#> 
#> ✔ No static columns. 
#> 
#> ✔ No duplicate columns.
#> 
#> ✔ No target values are missing. 
#> 
#> ✔ No predictor values are missing. 
#> 
#> ✔ No issues with dimensionality. 
#> 
#> ✖ Strongly correlated, by Spearman rank, pairs of numerical values are: 
#>  
#>  Sepal.Length - Petal.Length: 0.81;
#>  Sepal.Length - Petal.Width: 0.79;
#>  Petal.Length - Petal.Width: 0.98;
#> 
#> ✔ No strongly correlated, by Crammer's V rank, pairs of categorical values. 
#> 
#> ✔ No outliers in the dataset. 
#> 
#> ✖ Target data is not evenly distributed with quantile bins: 0.22 0.28 0.18 0.32 
#> 
#> ✔ Columns names suggest that none of them are IDs. 
#> 
#> ✔ Columns data suggest that none of them are IDs. 
#> 
#>  -------------------- CHECK DATA REPORT END -------------------- 
#>  
#> ✔ Data preprocessed. 
#> ✔ Data split and balanced. 
#> ✔ Correct formats prepared. 
#> ✔ Models successfully trained. 
#> ✔ Predicted successfully. 
save(train)
#> File: "_forester_12_3_23_12_37_53_.RData" saved successfully.