Tools for working with H2O AutoML resultsSource:
Functions that returns a tibble describing model performances.
rank_results()ranks average cross validation performances of candidate models on each metric.
collect_metrics()computes average statistics of performance metrics (summarized) for each model, or raw value in each resample (unsummarized).
tidy()computes average performance for each model.
member_weights()computes member importance for stacked ensemble models, i.e., the relative importance of base models in the meta-learner. This is typically the coefficient magnitude in the second-level GLM model.
refit() re-fits an existing AutoML model to add more candidates. The model
to be re-fitted needs to have engine argument
save_data = TRUE, and
keep_cross_validation_predictions = TRUE if stacked ensembles is needed for
# S3 method for workflow rank_results(x, ...) # S3 method for `_H2OAutoML` rank_results(x, ...) # S3 method for H2OAutoML rank_results(x, n = NULL, id = NULL, ...) # S3 method for workflow collect_metrics(x, ...) # S3 method for `_H2OAutoML` collect_metrics(x, ...) # S3 method for H2OAutoML collect_metrics(x, summarize = TRUE, n = NULL, id = NULL, ...) # S3 method for workflow tidy(x, ...) # S3 method for `_H2OAutoML` tidy(x, n = NULL, id = NULL, keep_model = TRUE, ...) get_leaderboard(x, n = NULL, id = NULL) member_weights(x, ...) # S3 method for `_H2OAutoML` extract_fit_parsnip(x, id = NULL, ...) # S3 method for `_H2OAutoML` extract_fit_engine(x, id = NULL, ...) # S3 method for workflow refit(object, ...) # S3 method for `_H2OAutoML` refit(object, verbosity = NULL, ...)
An integer for the number of top models to extract from AutoML results, default to all.
A character vector of model ids to retrieve.
A logical; should metrics be summarized over resamples (TRUE) or return the values for each individual resample.
A logical value for if the actual model object should be retrieved from the server. Defaults to
- object, x
auto_ml()model or workflow.
Verbosity of the backend messages printed during training; Must be one of NULL (live log disabled), "debug", "info", "warn", "error". Defaults to NULL.
H2O associates with each model in AutoML an unique id. This can be used for
model extraction and prediction, i.e.,
extract_fit_engine(x, id = id)
returns the model and
predict(x, id = id) will predict for that model.
extract_fit_parsnip(x, id = id) wraps the h2o model with parsnip
parsnip model object is discouraged.
algorithm column corresponds to the model family H2O use for a
particular model, including xgboost (
gradient boosting (
"GBM"), random forest and variants (
generalized linear model (
"GLM"), and neural network (
See the details section in
h2o::h2o.automl() for more information.