single_node_mode

  • Available in: CoxPH, Deep Learning

  • Hyperparameter: no

Description

Specify whether to run on a single node for fine-tuning of model parameters. Running on a single node reduces the effect of network overhead for smaller datasets.

Example

R

Python

library(h2o)
h2o.init()

# Import the heart dataset:
heart <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# Build and train the model:
coxph.model <- h2o.coxph(x="age", event_column="event", start_column="start", stop_column="stop",training_frame=heart, single_node_mode=TRUE)

# Generate predictions:
pred <- h2o.predict(object=coxph.model, newdata=heart)

import h2o
from h2o.estimators import H2OCoxProportionalHazardsEstimator
h2o.init()

# Import the heart dataset:
heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# Build and train the model:
coxph = H2OCoxProportionalHazardsEstimator(start_column="start", stop_column="stop", single_node_mode=True)
coxph.train(x="age", y="event", training_frame=heart)

# Generate predictions:
pred = coxph.predict(test_data=heart)