Given a gpb.Booster
, return evaluation results for a
particular metric on a particular dataset.
gpb.get.eval.result(booster, data_name, eval_name, iters = NULL,
is_err = FALSE)
numeric vector of evaluation result
Object of class gpb.Booster
Name of the dataset to return evaluation results for.
Name of the evaluation metric to return results for.
An integer vector of iterations you want to get evaluation results for. If NULL (the default), evaluation results for all iterations will be returned.
TRUE will return evaluation error instead
# \donttest{
# train a regression model
data(agaricus.train, package = "gpboost")
train <- agaricus.train
dtrain <- gpb.Dataset(train$data, label = train$label)
data(agaricus.test, package = "gpboost")
test <- agaricus.test
dtest <- gpb.Dataset.create.valid(dtrain, test$data, label = test$label)
params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- gpb.train(
params = params
, data = dtrain
, nrounds = 5L
, valids = valids
, min_data = 1L
, learning_rate = 1.0
)
# Examine valid data_name values
print(setdiff(names(model$record_evals), "start_iter"))
# Examine valid eval_name values for dataset "test"
print(names(model$record_evals[["test"]]))
# Get L2 values for "test" dataset
gpb.get.eval.result(model, "test", "l2")
# }
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