max_iterations

  • Available in: GLM, GAM, PCA, GLRM, K-Means, CoxPH

  • Hyperparameter: yes

Description

This option specifies the maximum allowed number of iterations (passes over data) during model training. This value must be between 1 and 1e6, inclusive.

Example

R

Python

library(h2o)
h2o.init()

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

# set the predictor names and the response column name
predictors <- c("displacement","power","weight","acceleration","year")
response <- "economy_20mpg"

# split into train and validation
cars.splits <- h2o.splitFrame(data = cars, ratios = .8)
train <- cars.splits[[1]]
valid <- cars.splits[[2]]

# try using the `max_iterations` parameter:
car_glm <- h2o.glm(x = predictors, y = response, family = 'binomial', training_frame = train, validation_frame = valid,
                   max_iterations = 50)

# print the auc for your validation data
print(h2o.auc(car_glm, valid = TRUE))

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

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

# set the predictor names and the response column name
predictors = ["displacement","power","weight","acceleration","year"]
response = "economy_20mpg"

# split into train and validation sets
train, valid = cars.split_frame(ratios = [.8])

# try using the `max_iterations` parameter:
# Initialize and train a GLM
cars_glm = H2OGeneralizedLinearEstimator(family = 'binomial', max_iterations = 50)
cars_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the auc for the validation data
cars_glm.auc(valid = True)