Changing the Column Type¶

H2O algorithms will treat a problem as a classification problem if the column type is factor and a regression problem if the column type is numeric. You can force H2O to use either classification or regression by changing the column type.

library(h2o)
h2o.init()

# import the boston housing dataset:
boston <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

# check the column type for the `chas` column
h2o.isnumeric(boston["chas"])
[1] TRUE

# change the column type to a factor
boston["chas"] <- as.factor(boston["chas"])
# verify that the column is now a factor
h2o.isfactor(boston["chas"])
[1] TRUE

# change the column type back to numeric
boston["chas"] <- as.numeric(boston["chas"])
# verify that the column is numeric and not a factor
h2o.isfactor(boston["chas"])
[1] FALSE
h2o.isnumeric(boston["chas"])
[1] TRUE

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
h2o.init()

# import the boston dataset:
boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

# check the column type for the `chas` column
boston["chas"].isnumeric()
[True]
# change the column type to a factor
boston['chas'] = boston['chas'].asfactor()
# verify that the column is now a factor
boston["chas"].isfactor()
[True]

# change the column type back to numeric
boston["chas"] = boston["chas"].asnumeric()
# verify that the column is numeric and not a factor
boston["chas"].isfactor()
[False]
boston["chas"].isnumeric()
[True]