details_logistic_reg_h2o: Logistic regression via h2o

In parsnip: A Common API to Modeling and Analysis Functions

Logistic regression via h2o

Description

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().

Saving fitted model objects

Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.

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