monotone_constraints

• Available in: 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: This option can only be used when the distribution is either gaussian or bernoulli.

Related Parameters

• None

Example

• r
• python

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)