Automatically prep a data.frame/tibble for use in the xgboost 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::reciep()function.- .recipe_formula
The formula that is going to be passed. For example if you are using the
diamondsdata then the formula would most likely be something likeprice ~ .
Details
This function will automatically prep your data.frame/tibble for use in the XGBoost algorithm.
This function will output a recipe specification.
See also
https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html
Other Preprocessor:
hai_c50_data_prepper(),
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()
Examples
library(ggplot2)
# Regression
hai_xgboost_data_prepper(.data = diamonds, .recipe_formula = price ~ .)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 9
#>
#> ── Operations
#> • Factor variables from: tidyselect::vars_select_helpers$where(is.character)
#> • Novel factor level assignment for: recipes::all_nominal_predictors()
#> • Dummy variables from: recipes::all_nominal_predictors()
#> • Zero variance filter on: recipes::all_predictors()
reg_obj <- hai_xgboost_data_prepper(diamonds, price ~ .)
get_juiced_data(reg_obj)
#> # A tibble: 53,940 × 27
#> carat depth table x y z price cut_1 cut_2 cut_3 cut_4 cut_5
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.23 61.5 55 3.95 3.98 2.43 326 0.359 -0.109 -0.522 -0.567 -0.315
#> 2 0.21 59.8 61 3.89 3.84 2.31 326 0.120 -0.436 -0.298 0.378 0.630
#> 3 0.23 56.9 65 4.05 4.07 2.31 327 -0.359 -0.109 0.522 -0.567 0.315
#> 4 0.29 62.4 58 4.2 4.23 2.63 334 0.120 -0.436 -0.298 0.378 0.630
#> 5 0.31 63.3 58 4.34 4.35 2.75 335 -0.359 -0.109 0.522 -0.567 0.315
#> 6 0.24 62.8 57 3.94 3.96 2.48 336 -0.120 -0.436 0.298 0.378 -0.630
#> 7 0.24 62.3 57 3.95 3.98 2.47 336 -0.120 -0.436 0.298 0.378 -0.630
#> 8 0.26 61.9 55 4.07 4.11 2.53 337 -0.120 -0.436 0.298 0.378 -0.630
#> 9 0.22 65.1 61 3.87 3.78 2.49 337 -0.598 0.546 -0.373 0.189 -0.0630
#> 10 0.23 59.4 61 4 4.05 2.39 338 -0.120 -0.436 0.298 0.378 -0.630
#> # ℹ 53,930 more rows
#> # ℹ 15 more variables: color_1 <dbl>, color_2 <dbl>, color_3 <dbl>,
#> # color_4 <dbl>, color_5 <dbl>, color_6 <dbl>, color_7 <dbl>,
#> # clarity_1 <dbl>, clarity_2 <dbl>, clarity_3 <dbl>, clarity_4 <dbl>,
#> # clarity_5 <dbl>, clarity_6 <dbl>, clarity_7 <dbl>, clarity_8 <dbl>
# Classification
hai_xgboost_data_prepper(Titanic, Survived ~ .)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 4
#>
#> ── Operations
#> • Factor variables from: tidyselect::vars_select_helpers$where(is.character)
#> • Novel factor level assignment for: recipes::all_nominal_predictors()
#> • Dummy variables from: recipes::all_nominal_predictors()
#> • Zero variance filter on: recipes::all_predictors()
cla_obj <- hai_xgboost_data_prepper(Titanic, Survived ~ .)
get_juiced_data(cla_obj)
#> # A tibble: 32 × 7
#> n Survived Class_X2nd Class_X3rd Class_Crew Sex_Male Age_Child
#> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 No 0 0 0 1 1
#> 2 0 No 1 0 0 1 1
#> 3 35 No 0 1 0 1 1
#> 4 0 No 0 0 1 1 1
#> 5 0 No 0 0 0 0 1
#> 6 0 No 1 0 0 0 1
#> 7 17 No 0 1 0 0 1
#> 8 0 No 0 0 1 0 1
#> 9 118 No 0 0 0 1 0
#> 10 154 No 1 0 0 1 0
#> # ℹ 22 more rows