5.1 Saving and loading models🔗ℹ

save-model writes the trained tree ensemble to a file; XGBoost picks the format from the extension (use ".json" or ".ubj"). load-model reads it back into a fresh booster:

(save-model booster "model.json")

(define reloaded (load-model "model.json"))

To keep the model in memory instead of on disk, serialize to bytes. save-model-to-bytes defaults to compact UBJSON; pass #:format "json" for the textual form. load-model-from-bytes reverses either:

(define blob (save-model-to-bytes booster))           ; UBJSON

(define json (save-model-to-bytes booster #:format "json"))

(define restored (load-model-from-bytes blob))

A model loaded by any of these routes produces the same predictions as the original booster.

save-model persists only the trained trees. To checkpoint mid-training and resume per-iteration updates in lockstep, use the full-state snapshots described in Global Configuration, Snapshots, and GPU.

5.2 Early stopping🔗ℹ

XGBoost’s Python package offers a built-in early-stopping callback. The Racket API has no callback mechanism; instead you drive the boosting loop yourself and decide when to stop. Build an untrained booster with #:rounds 0 (which still binds dtrain and the #:evals DMatrices into its cache), then step it one round at a time with booster-update-one-iter!. After each round, eval-one-iter returns XGBoost’s metric line, which parse-eval-line turns into a hash:

(define booster

(train dtrain

#:evals (list (cons "eval" deval))

#:objective "reg:squarederror"

#:max-depth 3

#:eta 0.1

#:verbosity 0

#:rounds 0))

(define eval-set (list (cons "train" dtrain) (cons "eval" deval)))

(for ([iter (in-range 30)])

(booster-update-one-iter! booster iter dtrain)

(define metrics (parse-eval-line (eval-one-iter booster iter eval-set)))

(printf "round ~a  eval-rmse ~a\n"

iter (hash-ref metrics "eval-rmse")))

Because you own the loop, early stopping is just ordinary Racket control flow: track the best metric so far and break out (or stop updating) once it fails to improve for a chosen number of rounds. The booster after N calls to booster-update-one-iter! is exactly an N-round model, and booster-slice can later extract the best-iteration prefix if you trained past it.