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Automatic machine learning via h2o

Source: R/auto_ml_h2o.R
details_auto_ml_h2o.Rd

h2o::h2o.automl defines an automated model training process and returns a leaderboard of models with best performances.

Details

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

Tuning Parameters

This model has no tuning parameters.

Engine arguments of interest

  • max_runtime_secs and max_models: controls the maximum running time and number of models to build in the automatic process.

  • exclude_algos and include_algos: a character vector indicating the excluded or included algorithms during model building. To see a full list of supported models, see the details section in h2o::h2o.automl().

  • validation: An integer between 0 and 1 specifying the proportion of training data reserved as validation set. This is used by h2o for performance assessment and potential early stopping.

Translation from parsnip to the original package (regression)

agua::h2o_train_auto() is a wrapper around h2o::h2o.automl().

auto_ml() %>%  
  set_engine("h2o") %>% 
  set_mode("regression") %>% 
  translate()

## Automatic Machine Learning Model Specification (regression)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), verbosity = NULL)

Translation from parsnip to the original package (classification)

auto_ml() %>%  
  set_engine("h2o") %>% 
  set_mode("classification") %>% 
  translate()

## Automatic Machine Learning Model Specification (classification)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), verbosity = NULL)

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.

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.