The H2O AutoML Leaderboard lists any models that have been created during the automl_reg()
training process.
The training process automatically uses the top model.
The available models can be shown with automl_leaderboard()
The model change the model used using automl_update_model()
.
automl_leaderboard(object) automl_update_model(object, model_id)
object | An object created by |
---|---|
model_id | An H2O Model ID (shown in the AutoML Leaderboard). Alternatively, the user can provide an H2O model. |
automl_leaderboard()
: A tibble
containing the H2O AutoML Leaderboard
automl_update_model()
: An updated parnsip
or workflow
with the H2O Model updated
if (FALSE) { library(tidymodels) library(modeltime.h2o) library(h2o) library(tidyverse) library(timetk) h2o.init( nthreads = -1, ip = 'localhost', port = 54321 ) # Model Spec model_spec <- automl_reg(mode = 'regression') %>% set_engine( engine = 'h2o', max_runtime_secs = 5, max_runtime_secs_per_model = 4, nfolds = 5, max_models = 3, exclude_algos = c("DeepLearning"), seed = 786 ) # Fit AutoML model_fit <- model_spec %>% fit(value ~ ., data = training(m750_splits)) # Inspect the Leaderboard leaderboard_tbl <- automl_leaderboard(model_fit) leaderboard_tbl # Swap an H2O Model Out (Using the 2nd model from the leaderboard) model_id_2 <- leaderboard_tbl$model_id[[2]] model_fit_2 <- automl_update_model(model_fit, model_id_2) model_fit_2 # Shutdown H2O when Finished. # Make sure to save any work before. h2o.shutdown(prompt = FALSE) }