GLM getGLMRegularizationPath errors out if standardize = False

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Wendy Wong commented: [~accountid:5fa438f822f3990076aa232d] notified me of the following problem:

{noformat}import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator as glm
h2o.init()

import the boston dataset:

this dataset looks at features of the boston suburbs and predicts median housing prices

the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing

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

set the predictor names and the response column name

predictors = boston.columns[:-1]

set the response column to "medv", the median value of owner-occupied homes in $1000's

response = "medv"

convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))

boston['chas'] = boston['chas'].asfactor()

split into train and validation sets

train, valid = boston.split_frame(ratios = [.8])

try using the standardize parameter:

initialize the estimator then train the model

boston_glm = H2OGeneralizedLinearEstimator(standardize = False)
boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

r = glm.getGLMRegularizationPath(boston_glm){noformat}

Thank you Arun.

[h2o-ops]

JIRA Issue Details

Jira Issue: PUBDEV-8602
Assignee: Wendy Wong
Reporter: Wendy Wong
State: Resolved
Fix Version: 3.36.1.1
Attachments: N/A
Development PRs: Available

[h2o-ops]

Linked PRs from JIRA

#6098