# objective_epsilon¶

• Available in: GLM
• Hyperparameter: no

## Description¶

GLM includes three criteria outside of max_iterations that define and check for convergence during logistic regression:

• beta_epsilon: Converge if the beta change is less than this value (or if beta stops changing). This is used by solvers.
• gradient_epsilon: Converge if the gradient value change is less than this value (using L-infinity norm). This is used when solver=L-BFGS.
• objective_epsilon: Converge if the relative objective value changes (for example, (old_val - new_val)/old_val). This is used by all solvers.

The default for these options is based on a heurisitic:

• The default for beta_epsilon is 1e-4.
• The default for gradient_epsilon is 1e-6 if there is no regularization (lambda=0) or you are running with lambda search; 1e-4 otherwise.
• The default for objective_epsilon is 1e-6 if lambda=0; 1e-4 otherwise.

## Example¶

library(h2o)
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.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

# set the predictor names and the response column name
predictors <- colnames(boston)[1:13]
# 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"] <- as.factor(boston["chas"]) # split into train and validation sets boston.splits <- h2o.splitFrame(data = boston, ratios = .8) train <- boston.splits[[1]] valid <- boston.splits[[2]] # try using the objective_epsilon parameter: # train your model, where you specify objective_epsilon boston_glm <- h2o.glm(x = predictors, y = response, training_frame = train, validation_frame = valid, objective_epsilon = 1e-3) # print the mse for the validation data print(h2o.mse(boston_glm, valid=TRUE))  import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator 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 objective_epsilon parameter:
# initialize the estimator then train the model
boston_glm = H2OGeneralizedLinearEstimator(objective_epsilon = 1e-3)
boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the mse for validation set
print(boston_glm.mse(valid=True))