Construct a learner — learner_xgboost • targeted

Constructs a learner class object for xgboost::xgboost.

Usage

learner_xgboost(
  formula,
  max_depth = 2L,
  eta = 1,
  nrounds = 2L,
  subsample = 1,
  lambda = 1,
  verbose = 0,
  objective = "reg:squarederror",
  info = paste("xgboost", objective),
  learner.args = NULL,
  ...
)

Arguments

formula: (formula) Formula specifying response and design matrix.
max_depth: (integer) Maximum depth of a tree.
eta: (numeric) Learning rate.
nrounds: max number of boosting iterations.
subsample: (numeric) Subsample ratio of the training instance.
lambda: (numeric) L2 regularization term on weights.
verbose: If 0, xgboost will stay silent. If 1, it will print information about performance. If 2, some additional information will be printed out. Note that setting verbose > 0 automatically engages the cb.print.evaluation(period=1) callback function.
objective: (character) Specify the learning task and the corresponding learning objective. See xgboost::xgboost for all available options.
info: (character) Optional information to describe the instantiated learner object.
learner.args: (list) Additional arguments to learner$new().
...: Additional arguments to xgboost::xgboost.

Value

learner object.

Examples

n  <- 1e3
x1 <- rnorm(n, sd = 2)
x2 <- rnorm(n)
lp <- x2*x1 + cos(x1)
yb <- rbinom(n, 1, lava::expit(lp))
y <-  lp + rnorm(n, sd = 0.5**.5)
d0 <- data.frame(y, yb, x1, x2)

# regression
lr <- learner_xgboost(y ~ x1 + x2, nrounds = 5)
lr$estimate(d0)
lr$predict(head(d0))
#> [1] -1.9827677  2.0304260  2.0304260  0.1772428  1.5559609 -3.3161051

# binary classification
lr <- learner_xgboost(yb ~ x1 + x2, nrounds = 5,
 objective = "binary:logistic"
)
lr$estimate(d0)
lr$predict(head(d0))
#> [1] 0.2974974 0.9214302 0.7824602 0.1971165 0.4704079 0.1971165

# multi-class classification
d0 <- iris
d0$y <- as.numeric(d0$Species)- 1

lr <- learner_xgboost(y ~ ., objective = "multi:softprob", num_class = 3)
lr$estimate(d0)
lr$predict(head(d0))
#>           [,1]       [,2]       [,3]
#> [1,] 0.9290679 0.03546607 0.03546607
#> [2,] 0.9290679 0.03546607 0.03546607
#> [3,] 0.9290679 0.03546607 0.03546607
#> [4,] 0.9290679 0.03546607 0.03546607
#> [5,] 0.9290679 0.03546607 0.03546607
#> [6,] 0.9290679 0.03546607 0.03546607