details_rule_fit_h2o: RuleFit models via h2o

In parsnip: A Common API to Modeling and Analysis Functions

RuleFit models via h2o

Description

h2o::h2o.rulefit() fits a model that derives simple feature rules from a tree ensemble and uses the rules as features to a regularized (LASSO) model. agua::h2o_train_rule() is a wrapper around this function.

Details

For this engine, there are multiple modes: classification and regression

Tuning Parameters

This model has 3 tuning parameters:

• trees: # Trees (type: integer, default: 50L)

• tree_depth: Tree Depth (type: integer, default: 3L)

• penalty: Amount of Regularization (type: double, default: 0) Note that penalty for the h2o engine in 'rule_fit()“ corresponds to the L1 penalty (LASSO).

Other engine arguments of interest:

• algorithm: The algorithm to use to generate rules. should be one of “AUTO”, “DRF”, “GBM”, defaults to “AUTO”.

• min_rule_length: Minimum length of tree depth, opposite of tree_dpeth, defaults to 3.

• max_num_rules: The maximum number of rules to return. The default value of -1 means the number of rules is selected by diminishing returns in model deviance.

• model_type: The type of base learners in the ensemble, should be one of: “rules_and_linear”, “rules”, “linear”, defaults to “rules_and_linear”.

Translation from parsnip to the underlying model call (regression)

agua::h2o_train_rule() is a wrapper around h2o::h2o.rulefit().

The agua extension package is required to fit this model.

library(rules)

rule_fit(
  trees = integer(1),
  tree_depth = integer(1),
  penalty = numeric(1)
) %>%
  set_engine("h2o") %>%
  set_mode("regression") %>%
  translate()

## RuleFit Model Specification (regression)
## 
## Main Arguments:
##   trees = integer(1)
##   tree_depth = integer(1)
##   penalty = numeric(1)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), rule_generation_ntrees = integer(1), 
##     max_rule_length = integer(1), lambda = numeric(1))

Translation from parsnip to the underlying model call (classification)

agua::h2o_train_rule() for rule_fit() is a wrapper around h2o::h2o.rulefit().

The agua extension package is required to fit this model.

rule_fit(
  trees = integer(1),
  tree_depth = integer(1),
  penalty = numeric(1)
) %>%
  set_engine("h2o") %>%
  set_mode("classification") %>%
  translate()

## RuleFit Model Specification (classification)
## 
## Main Arguments:
##   trees = integer(1)
##   tree_depth = integer(1)
##   penalty = numeric(1)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), rule_generation_ntrees = integer(1), 
##     max_rule_length = integer(1), lambda = numeric(1))

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.

Other details

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 Aug. 18, 2023, 1:07 a.m.