Trying to train a decision tree
from hpsklearn import HyperoptEstimator, decision_tree
[...load data....]
estim = HyperoptEstimator(classifier=decision_tree('dt'))
estim.fit(X, y)
yields the following exception:
ERROR:hyperopt.fmin:job exception: min_samples_leaf must be at least 1 or in (0, 0.5], got 4.0
Since a float value of min_samples_leaf is interpreted as a percentage, this seems like possibly a failure to cast to int somewhere in the hpsklearn code.
Trying to train a decision tree
yields the following exception:
Since a float value of
min_samples_leafis interpreted as a percentage, this seems like possibly a failure to cast to int somewhere in the hpsklearn code.