solver

  • Available in: GLM
  • Hyperparameter: no

Description

The solver option allows you to specify the solver method to use in GLM. When specifying a solver, the optimal solver depends on the data properties and prior information regarding the variables (if available). In general, the data are considered sparse if the ratio of zeros to non-zeros in the input matrix is greater than 10. The solution is sparse when only a subset of the original set of variables is intended to be kept in the model. In a dense solution, all predictors have non-zero coefficients in the final model.

In GLM, you can specify one of the following solvers:

  • IRLSM: Iteratively Reweighted Least Squares Method
  • L_BFGS: Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm
  • AUTO: Sets the solver based on given data and parameters (default)
  • COORDINATE_DESCENT: Coordinate Decent
  • COORDINATE_DESCENT_NAIVE: Coordinate Decent Naive

Detailed information about each of these optiosn is available in the Solvers section.

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 `solver` parameter:
boston_glm <- h2o.glm(x = predictors, y = response, training_frame = train,
                      validation_frame = valid,
                      solver = 'IRLSM')

# 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 `solver` parameter:
# initialize the estimator then train the model
boston_glm = H2OGeneralizedLinearEstimator(solver = 'irlsm')
boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

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