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.
extract_fit_engine()
extracts single candidate model from auto_ml()
results. When id
is null, it returns the leader 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
later models.
Usage
# 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 `_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, ...)
Arguments
- ...
Not used.
- n
An integer for the number of top models to extract from AutoML results, default to all.
- id
A character vector of model ids to retrieve.
- summarize
A logical; should metrics be summarized over resamples (TRUE) or return the values for each individual resample.
- keep_model
A logical value for if the actual model object should be retrieved from the server. Defaults to
TRUE
.- object, x
A fitted
auto_ml()
model or workflow.- verbosity
Verbosity of the backend messages printed during training; Must be one of NULL (live log disabled), "debug", "info", "warn", "error". Defaults to NULL.
Details
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.
The algorithm
column corresponds to the model family H2O use for a
particular model, including xgboost ("XGBOOST"
),
gradient boosting ("GBM"
), random forest and variants ("DRF"
, "XRT"
),
generalized linear model ("GLM"
), and neural network ("deeplearning"
).
See the details section in h2o::h2o.automl()
for more information.
Examples
if (h2o_running()) {
auto_fit <- auto_ml() %>%
set_engine("h2o", max_runtime_secs = 5) %>%
set_mode("regression") %>%
fit(mpg ~ ., data = mtcars)
rank_results(auto_fit, n = 5)
collect_metrics(auto_fit, summarize = FALSE)
tidy(auto_fit)
member_weights(auto_fit)
}