Hello everyone,
I performed a parameters optimization in mlr3 and then pass it to the
benchmark to compare the optimized and a baseline learner, but it give an
error message and does not recognize the optimized (i.e. at) learner.
df=readARFF("nasa93.arff")
task=TaskRegr$new("df", df, target = "act_effort")
learner= lrn("regr.rpart")
resampling = rsmp("bootstrap", repeats=100)
search_space = paradox::ParamSet$new(
params = list(paradox::ParamDbl$new("cp", lower = 0.001, upper = 0.1)))
terminator = trm("evals", n_evals = 5)
tuner = tnr("grid_search", resolution = 10)
at = AutoTuner$new(
learner = learner,
resampling = resampling,
measure = measure,
search_space = search_space,
terminator = terminator,
tuner = tuner
)
grid = benchmark_grid(task = task, learner = list(lrn("at"),
lrn("regr.rpart")),resampling)
bmr = benchmark(grid)
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