``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
- COORDINATE_DESCENT: Coordinate Decent
- COORDINATE_DESCENT_NAIVE: Coordinate Decent Naive
- AUTO: Sets the solver based on given data and parameters (default)
Detailed information about each of these options is available in the `Solvers <../glm.html#solvers>`__ section. The bullets below describe GLM chooses the solver when ``solver=AUTO``:
- If there are more than 5k active predictors, GLM uses L_BFGS.
- If ``family=multinomial`` and ``alpha=0`` (ridge or no penalty), GLM uses L_BFGS.
- If lambda search is enabled, GLM uses COORDINATE_DESCENT.
- If your data has upper/lower bounds and no proximal penlaty, GLM uses COORDINATE_DESCENT.
- If none above is true, then GLM defaults to IRLSM. This is because COORDINATE_DESCENT works much better with lambda search.
Below are some general guidelines to follow when specifying a solver.
- L_BFGS works much better for L2-only multininomial and if you have too many active predictors.
- You must use IRLSM if you have p-values.
- IRLSM and COORDINATE_DESCENT share the same path (i.e., they both compute the same gram matrix), they just solve it differently.
- Use COORDINATE_DESCENT if you have less than 5000 predictors and L1 penalty.
- COORDINATE_DESCENT performs better when ``lambda_search`` is enabled. Also with bounds, it tends to get a higher accuracy.
Related Parameters
~~~~~~~~~~~~~~~~~~
- `alpha `__
- `lambda `__
- `lambda_search `__
Example
~~~~~~~
.. example-code::
.. code-block:: r
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))
.. code-block:: python
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))