`monotone_constraints`¶

Available in: AutoML, GBM, XGBoost
Hyperparameter: no

Description¶

A mapping that represents monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. Note that constraints can only be defined for numerical columns.

Note: In GBM and XGBoost, this option can only be used when the distribution is gaussian, bernoulli, tweedie. In GBM also quantile distribution is supported.

You can enable monotone constraints consistency check using the system property: sys.ai.h2o.tree.constraintConsistencyCheck=true. It checks the parent prediction is in interval of the children predictions. It is disabled by default.

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 monotone_constraints option
prostate_gbm <- h2o.gbm(y = response,
                        monotone_constraints = list(AGE = 1),
                        seed = 1234,
                        training_frame = prostate)

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
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 monotone_constraints option
monotone_constraints={"AGE":1}
gbm_model = H2OGradientBoostingEstimator(seed=seed, monotone_constraints=monotone_constraints)
gbm_model.train(y=response, ignored_columns=["ID"], training_frame=prostate)