``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. Related Parameters ~~~~~~~~~~~~~~~~~~ - None Example ~~~~~~~ .. tabs:: .. code-tab:: r R 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) .. code-tab:: python 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)