interaction_constraints
¶
Available in: XGBoost
Hyperparameter: no
Description¶
Specify the feature column interactions which are allowed to interact during tree building. Use column names to define which features can interact together. This option defaults to None/Null, which means that all column features are included.
Note: This option can only be used when the categorical encoding is set to AUTO
(one_hot_internal
or OneHotInternal
).
Example¶
library(h2o)
h2o.init()
# import the prostate dataset:
prostate = h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
# convert the CAPSULE column to a factor
prostate$CAPSULE <- as.factor(prostate$CAPSULE)
response <- "CAPSULE"
# train a model using the interaction_constraints option
prostate_xgb <- h2o.xgboost(y = response,
interaction_constraints = list(list("AGE", "DCAPS")),
seed = 1234,
training_frame = prostate)
import h2o
from h2o.estimators.xgboost import H2OXGBoostEstimator
h2o.init()
# import the prostate dataset:
prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
# convert the CAPSULE column to a factor
prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
response = "CAPSULE"
seed = 1234
# train a model using the interaction_constraints option
xgb_model = H2OXGBoostEstimator(seed=seed, interaction_constraints=[["AGE", "DCAPS"]])
xgb_model.train(y=response, ignored_columns=["ID"], training_frame=prostate)