4.1 The #:params bundle🔗ℹ

#:params takes a hash or association list of parameters. Keys may be strings, symbols, or keywords; symbol and keyword keys are normalized to XGBoost’s underscore style (hyphens become underscores), and values are converted to strings before reaching the native layer:

(train dtrain

#:params '((objective  . "reg:squarederror")

(max-depth  . 3)

(eta        . 0.1)

(tree-method . "hist")))

Here 'max-depth reaches XGBoost as "max_depth", and the numeric 3 becomes "3".

4.2 Keyword conveniences🔗ℹ

The most common parameters also have dedicated keywords on train: #:objective, #:eta, #:max-depth, #:num-class, #:eval-metric, and #:verbosity. They are applied after #:params, so a keyword overrides any same-named entry in the bundle:

(train dtrain

#:objective "binary:logistic"

#:max-depth 3

#:eta 0.3

#:verbosity 0

#:rounds 30)

4.3 Evaluation sets🔗ℹ

To watch performance on held-out data during training, pass #:evals — a list of (cons name dmatrix) pairs. XGBoost reports each named metric per round, and the names you choose appear in the metric lines:

(train dtrain

#:evals (list (cons "train" dtrain)

(cons "eval"  deval))

#:objective "reg:squarederror"

#:eval-metric "rmse"

#:rounds 30)

Driving the evaluation loop yourself — to log metrics or stop early — is covered in Training.

4.4 Updating a booster’s parameters🔗ℹ

A parameter can also be set on an existing booster with booster-set-param!, which uses the same key/value coercion as #:params:

(booster-set-param! booster "tree_method" "hist")