Package: agua 0.1.4.9000

Qiushi Yan

agua:'tidymodels' Integration with 'h2o'

Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.

Authors:Max Kuhn [aut], Qiushi Yan [aut, cre], Steven Pawley [aut], Posit Software, PBC [cph, fnd]

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agua/json (API)
NEWS

# Installagua in R:

install.packages('agua',repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/agua/issues

On CRAN:

35 exports 21 stars 2.39 score 87 dependencies 1.0k downloads

Last updated 23 days agofrom:dfc7a13ad3

Exports:%>%agua_backend_options as_h2o as_tibble autoplot collect_metrics extract_fit_engine extract_fit_parsnip get_leaderboard h2o_activation h2o_end h2o_get_frame h2o_get_model h2o_predict h2o_predict_classification h2o_predict_regression h2o_remove h2o_remove_all h2o_running h2o_split h2o_start h2o_train h2o_train_auto h2o_train_gbm h2o_train_glm h2o_train_mlp h2o_train_nb h2o_train_rf h2o_train_rule h2o_train_xgboost h2o_xgboost_available member_weights rank_results refit tidy

Dependencies:bitops class cli clock codetools colorspace cpp11 data.table diagram dials DiceDesign digest doFuture dplyr fansi farver foreach furrr future future.apply generics ggplot2 globals glue gower GPfit gtable h2o hardhat ipred isoband iterators jsonlite KernSmooth labeling lattice lava lhs lifecycle listenv lubridate magrittr MASS Matrix mgcv modelenv munsell nlme nnet numDeriv parallelly parsnip pillar pkgconfig prettyunits prodlim progressr purrr R6 RColorBrewer Rcpp RCurl recipes rlang rpart rsample scales shape slider SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tune tzdb utf8 vctrs viridisLite warp withr workflows yardstick

Introduction to agua

Rendered fromagua.Rmdusingknitr::rmarkdownon Jun 13 2024.

Last update: 2022-10-12
Started: 2022-07-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Control model tuning via 'h2o::h2o.grid()'	agua_backend_options
Data conversion tools	as_h2o as_tibble.H2OFrame
Plot rankings and metrics of H2O AutoML results	autoplot.H2OAutoML autoplot.workflow
Prediction wrappers for h2o	h2o_predict h2o_predict_classification h2o_predict_regression predict._H2OAutoML
Utility functions for interacting with the h2o server	h2o_end h2o_get_frame h2o_get_model h2o_remove h2o_remove_all h2o_running h2o_start h2o_xgboost_available
Model wrappers for h2o	h2o_train h2o_train_auto h2o_train_gbm h2o_train_glm h2o_train_mlp h2o_train_nb h2o_train_rf h2o_train_rule h2o_train_xgboost
Tools for working with H2O AutoML results	collect_metrics.H2OAutoML collect_metrics.workflow collect_metrics._H2OAutoML extract_fit_engine._H2OAutoML extract_fit_parsnip._H2OAutoML get_leaderboard member_weights rank_results.H2OAutoML rank_results.workflow rank_results._H2OAutoML refit.workflow refit._H2OAutoML tidy._H2OAutoML