diff --git a/R/boost_tree.R b/R/boost_tree.R
index cce9d1ccd..248fba8a0 100644
--- a/R/boost_tree.R
+++ b/R/boost_tree.R
@@ -432,11 +432,19 @@ as_xgb_data <- function(x, y, validation = 0, weights = NULL, event_level = "fir
       # Split data
       m <- floor(n * (1 - validation)) + 1
       trn_index <- sample(1:n, size = max(m, 2))
-      val_data <- xgboost::xgb.DMatrix(x[-trn_index,], label = y[-trn_index], missing = NA)
+      val_info_list <- list(label = y[-trn_index])
+      if (!is.null(weights) && inherits(weights, "hardhat_frequency_weights")) {
+        #Only pass weights to internal validation set if they are frequency weights
+        weights <- as.integer(weights)
+        val_info_list$weight <- weights[-trn_index]
+      }
+
+      val_data <- xgboost::xgb.DMatrix(x[-trn_index,], info = val_info_list, missing = NA)
       watch_list <- list(validation = val_data)
 
       info_list <- list(label = y[trn_index])
       if (!is.null(weights)) {
+        weights <- weights_to_numeric(weights, spec = list(engine = "xgboost"))
         info_list$weight <- weights[trn_index]
       }
       dat <- xgboost::xgb.DMatrix(x[trn_index,], missing = NA, info = info_list)
@@ -445,6 +453,7 @@ as_xgb_data <- function(x, y, validation = 0, weights = NULL, event_level = "fir
     } else {
       info_list <- list(label = y)
       if (!is.null(weights)) {
+        weights <- weights_to_numeric(weights, spec = list(engine = "xgboost"))
         info_list$weight <- weights
       }
       dat <- xgboost::xgb.DMatrix(x, missing = NA, info = info_list)
diff --git a/R/fit.R b/R/fit.R
index 6cda2e2c0..a43660cb2 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -266,7 +266,13 @@ fit_xy.model_spec <-
     eval_env <- rlang::env()
     eval_env$x <- x
     eval_env$y <- y
-    eval_env$weights <- weights_to_numeric(case_weights, object)
+
+    if(object$engine == "xgboost" && !is.null(case_weights)){
+      # Pass as raw to preserve weight type e.g. frequency, importance
+      eval_env$weights <- case_weights
+    } else {
+      eval_env$weights <- weights_to_numeric(case_weights, object)
+    }
 
     # TODO case weights: pass in eval_env not individual elements
     fit_interface <- check_xy_interface(eval_env$x, eval_env$y, cl, object)
diff --git a/tests/testthat/test_boost_tree_xgboost.R b/tests/testthat/test_boost_tree_xgboost.R
index 5adde2957..f63d09a24 100644
--- a/tests/testthat/test_boost_tree_xgboost.R
+++ b/tests/testthat/test_boost_tree_xgboost.R
@@ -360,7 +360,8 @@ test_that('xgboost data conversion', {
   mtcar_x <- mtcars[, -1]
   mtcar_mat <- as.matrix(mtcar_x)
   mtcar_smat <- Matrix::Matrix(mtcar_mat, sparse = TRUE)
-  wts <- 1:32
+  wts <- hardhat::importance_weights(1:32)
+  freq_wts <- hardhat::frequency_weights(1:32)
 
   expect_error(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg), regexp = NA)
   expect_true(inherits(from_df$data, "xgb.DMatrix"))
@@ -403,11 +404,16 @@ test_that('xgboost data conversion', {
 
   # case weights added
   expect_error(wted <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts), regexp = NA)
-  expect_equal(wts, xgboost::getinfo(wted$data, "weight"))
+  expect_equal(as.numeric(wts), xgboost::getinfo(wted$data, "weight"))
   expect_error(wted_val <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts, validation = 1/4), regexp = NA)
   expect_true(all(xgboost::getinfo(wted_val$data, "weight") %in% wts))
   expect_null(xgboost::getinfo(wted_val$watchlist$validation, "weight"))
 
+  # check that freq weights are passed to internal validation set
+  set.seed(1)
+  expect_error(val_freq_wts<-parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = freq_wts, validation = 1/10), regexp = NA)
+  expect_true(all(xgboost::getinfo(val_freq_wts$watchlist$validation, "weight") %in% c(3,17,26)))
+
 })
 
 
@@ -419,7 +425,8 @@ test_that('xgboost data and sparse matrices', {
   mtcar_x <- mtcars[, -1]
   mtcar_mat <- as.matrix(mtcar_x)
   mtcar_smat <- Matrix::Matrix(mtcar_mat, sparse = TRUE)
-  wts <- 1:32
+  wts <- hardhat::importance_weights(1:32)
+  freq_wts <- hardhat::frequency_weights(1:32)
 
   xgb_spec <-
     boost_tree(trees = 10) %>%
@@ -443,11 +450,16 @@ test_that('xgboost data and sparse matrices', {
 
   # case weights added
   expect_error(wted <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts), regexp = NA)
-  expect_equal(wts, xgboost::getinfo(wted$data, "weight"))
+  expect_equal(as.numeric(wts), xgboost::getinfo(wted$data, "weight"))
   expect_error(wted_val <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts, validation = 1/4), regexp = NA)
   expect_true(all(xgboost::getinfo(wted_val$data, "weight") %in% wts))
   expect_null(xgboost::getinfo(wted_val$watchlist$validation, "weight"))
 
+  # check that freq weights are passed to internal validation set
+  set.seed(1)
+  expect_error(val_freq_wts<-parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = freq_wts, validation = 1/10), regexp = NA)
+  expect_true(all(xgboost::getinfo(val_freq_wts$watchlist$validation, "weight") %in% c(3,17,26)))
+
 })