XGBoost models

In tidypredict: Run Predictions Inside the Database

if (requireNamespace("xgboost", quietly = TRUE)) {
  library(tidypredict)
  library(xgboost)
  library(dplyr)
  eval_code <- TRUE
} else {
  eval_code <- FALSE
}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = eval_code
)

| Function |Works| |---------------------------------------------------------------|-----| |tidypredict_fit(), tidypredict_sql(), parse_model() | ✔ | |tidypredict_to_column() | ✔ | |tidypredict_test() | ✔ | |tidypredict_interval(), tidypredict_sql_interval() | ✗ | |parsnip | ✔ |

tidypredict_ functions

library(xgboost)

logregobj <- function(preds, dtrain) {
  labels <- xgboost::getinfo(dtrain, "label")
  preds <- 1 / (1 + exp(-preds))
  grad <- preds - labels
  hess <- preds * (1 - preds)
  return(list(grad = grad, hess = hess))
}

xgb_bin_data <- xgboost::xgb.DMatrix(
  as.matrix(mtcars[, -9]),
  label = mtcars$am
)

model <- xgboost::xgb.train(
  params = list(max_depth = 2, objective = "binary:logistic", base_score = 0.5),
  data = xgb_bin_data,
  nrounds = 50
)

• Create the R formula r tidypredict_fit(model)

• Add the prediction to the original table ```r library(dplyr)

mtcars %>% tidypredict_to_column(model) %>% glimpse() ```

• Confirm that tidypredict results match to the model's predict() results. The xg_df argument expects the xgb.DMatrix data set. r tidypredict_test(model, mtcars, xg_df = xgb_bin_data)

Please be aware that xgboost converts data into 32-bit floats internally. This could possibly lead to splits being done incorrectly. Always verify that the predictions match up with model predictions. See this issue for more information.

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)

p_model <- boost_tree(mode = "regression") %>%
  set_engine("xgboost") %>%
  fit(am ~ ., data = mtcars)

tidypredict_test(p_model, mtcars, xg_df = xgb_bin_data)

Parse model spec

Here is an example of the model spec:

pm <- parse_model(model)
str(pm, 2)

str(pm$trees[1])

tidypredict documentation built on Dec. 13, 2025, 9:06 a.m.