`h2o::h2o.glm()`

fits a generalized linear model for binary outcomes.
A linear combination of the predictors is used to model the log odds of an
event.

## Details

For this engine, there is a single mode: classification

### Tuning Parameters

This model has 2 tuning parameters:

`mixture`

: Proportion of Lasso Penalty (type: double, default: see below)`penalty`

: Amount of Regularization (type: double, default: see below)

By default, when not given a fixed `penalty`

,
`h2o::h2o.glm()`

uses a heuristic approach to select
the optimal value of `penalty`

based on training data. Setting the
engine parameter `lambda_search`

to `TRUE`

enables an efficient version
of the grid search, see more details at
https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/lambda_search.html.

The choice of `mixture`

depends on the engine parameter `solver`

, which
is automatically chosen given training data and the specification of
other model parameters. When `solver`

is set to `'L-BFGS'`

, `mixture`

defaults to 0 (ridge regression) and 0.5 otherwise.

### Translation from parsnip to the original package

`agua::h2o_train_glm()`

for `logistic_reg()`

is
a wrapper around `h2o::h2o.glm()`

. h2o will
automatically picks the link function and distribution family or
binomial responses.

```
logistic_reg() %>%
set_engine("h2o") %>%
translate()
```

```
## Logistic Regression Model Specification (classification)
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## validation_frame = missing_arg(), family = "binomial")
```

To use a non-default argument in `h2o::h2o.glm()`

,
pass in as an engine argument to `set_engine()`

:

```
logistic_reg() %>%
set_engine("h2o", compute_p_values = TRUE) %>%
translate()
```

```
## Logistic Regression Model Specification (classification)
##
## Engine-Specific Arguments:
## compute_p_values = TRUE
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## validation_frame = missing_arg(), compute_p_values = TRUE,
## family = "binomial")
```

### Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via `fit()`

, parsnip will
convert factor columns to indicators.

Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.

By default, `h2o::h2o.glm()`

uses the argument
`standardize = TRUE`

to center and scale all numeric columns.

### Initializing h2o

To use the h2o engine with tidymodels, please run `h2o::h2o.init()`

first. By default, This connects R to the local h2o server. This needs
to be done in every new R session. You can also connect to a remote h2o
server with an IP address, for more details see
`h2o::h2o.init()`

.

You can control the number of threads in the thread pool used by h2o
with the `nthreads`

argument. By default, it uses all CPUs on the host.
This is different from the usual parallel processing mechanism in
tidymodels for tuning, while tidymodels parallelizes over resamples, h2o
parallelizes over hyperparameter combinations for a given resample.

h2o will automatically shut down the local h2o instance started by R
when R is terminated. To manually stop the h2o server, run
`h2o::h2o.shutdown()`

.