details_multinom_reg_h2o: Multinomial regression via h2o

In parsnip: A Common API to Modeling and Analysis Functions

Multinomial regression via h2o

Description

h2o::h2o.glm() fits a model that uses linear predictors to predict multiclass data for multinomial responses.

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 multinom_reg() is a wrapper around h2o::h2o.glm() with family = 'multinomial'.

multinom_reg(penalty = double(1), mixture = double(1)) %>% 
  set_engine("h2o") %>% 
  translate()

## Multinomial Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = double(1)
##   mixture = double(1)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), lambda = double(1), alpha = double(1), 
##     family = "multinomial")

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 the data.

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().

parsnip documentation built on June 24, 2024, 5:14 p.m.