Xgboost classification

[EmilHvitfeldt]

This PR adds hard and soft predictions for classification models for xgboost

library(parsnip)
library(orbital)

# better printing
iris <- tibble::as_tibble(iris)

bt_spec <- boost_tree(mode = "classification", engine = "xgboost")
bt_fit <- fit(bt_spec, Species ~ ., iris)

orb_obj <- orbital(bt_fit, type = "class")
predict(orb_obj, iris)
#> # A tibble: 150 × 1
#>    .pred_class
#>    <chr>      
#>  1 setosa     
#>  2 setosa     
#>  3 setosa     
#>  4 setosa     
#>  5 setosa     
#>  6 setosa     
#>  7 setosa     
#>  8 setosa     
#>  9 setosa     
#> 10 setosa     
#> # ℹ 140 more rows

orb_obj <- orbital(bt_fit, type = "prob")
predict(orb_obj, iris)
#> # A tibble: 150 × 3
#>    .pred_setosa .pred_versicolor .pred_virginica
#>           <dbl>            <dbl>           <dbl>
#>  1        0.984          0.00916         0.00716
#>  2        0.982          0.00915         0.00861
#>  3        0.984          0.00916         0.00716
#>  4        0.984          0.00916         0.00716
#>  5        0.984          0.00916         0.00716
#>  6        0.984          0.00916         0.00716
#>  7        0.984          0.00916         0.00716
#>  8        0.984          0.00916         0.00716
#>  9        0.982          0.00915         0.00861
#> 10        0.984          0.00916         0.00716
#> # ℹ 140 more rows

orb_obj <- orbital(bt_fit, type = c("class", "prob"))
predict(orb_obj, iris)
#> # A tibble: 150 × 4
#>    .pred_class .pred_setosa .pred_versicolor .pred_virginica
#>    <chr>              <dbl>            <dbl>           <dbl>
#>  1 setosa             0.984          0.00916         0.00716
#>  2 setosa             0.982          0.00915         0.00861
#>  3 setosa             0.984          0.00916         0.00716
#>  4 setosa             0.984          0.00916         0.00716
#>  5 setosa             0.984          0.00916         0.00716
#>  6 setosa             0.984          0.00916         0.00716
#>  7 setosa             0.984          0.00916         0.00716
#>  8 setosa             0.984          0.00916         0.00716
#>  9 setosa             0.982          0.00915         0.00861
#> 10 setosa             0.984          0.00916         0.00716
#> # ℹ 140 more rows

^{Created on 2024-12-16 with reprex v2.1.0}