Automatically prep a data.frame/tibble for use in the C5.0 algorithm.
Arguments
- .data
The data that you are passing to the function. Can be any type of data that is accepted by the
dataparameter of therecipes::recipe()function.- .recipe_formula
The formula that is going to be passed. For example if you are using the
irisdata then the formula would most likely be something likeSpecies ~ .
Details
This function will automatically prep your data.frame/tibble for use in the C5.0 algorithm. The C5.0 algorithm is a lazy learning classification algorithm. It expects data to be presented in a certain fashion.
This function will output a recipe specification.
See also
https://www.rulequest.com/see5-unix.html
Other Preprocessor:
hai_cubist_data_prepper(),
hai_data_impute(),
hai_data_poly(),
hai_data_scale(),
hai_data_transform(),
hai_data_trig(),
hai_earth_data_prepper(),
hai_glmnet_data_prepper(),
hai_knn_data_prepper(),
hai_ranger_data_prepper(),
hai_svm_poly_data_prepper(),
hai_svm_rbf_data_prepper(),
hai_xgboost_data_prepper()
Other C5.0:
hai_auto_c50()
Examples
library(ggplot2)
hai_c50_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 4
#>
#> ── Operations
#> • Factor variables from: tidyselect::vars_select_helpers$where(is.character)
rec_obj <- hai_c50_data_prepper(Titanic, Survived ~ .)
get_juiced_data(rec_obj)
#> # A tibble: 32 × 5
#> Class Sex Age n Survived
#> <fct> <fct> <fct> <dbl> <fct>
#> 1 1st Male Child 0 No
#> 2 2nd Male Child 0 No
#> 3 3rd Male Child 35 No
#> 4 Crew Male Child 0 No
#> 5 1st Female Child 0 No
#> 6 2nd Female Child 0 No
#> 7 3rd Female Child 17 No
#> 8 Crew Female Child 0 No
#> 9 1st Male Adult 118 No
#> 10 2nd Male Adult 154 No
#> # ℹ 22 more rows