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h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Update descriptions of AutoML in R and Python packages #8097

Closed exalate-issue-sync[bot] closed 1 year ago

exalate-issue-sync[bot] commented 1 year ago

These descriptions are out of date: [https://github.com/h2oai/h2o-3/blob/master/h2o-r/h2o-package/R/automl.R|https://github.com/h2oai/h2o-3/blob/master/h2o-r/h2o-package/R/automl.R] [https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/automl/autoh2o.py|https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/automl/autoh2o.py]

They are missing some of our new algos. Here’s a more up-to-date description of the algo (from AutoML User Guide FAQ):

The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. The current version of AutoML trains and cross-validates the following algorithms (in the following order): three pre-specified XGBoost GBM (Gradient Boosting Machine) models, a fixed grid of GLMs, a default Random Forest (DRF), five pre-specified H2O GBMs, a near-default Deep Neural Net, an Extremely Randomized Forest (XRT), a random grid of XGBoost GBMs, a random grid of H2O GBMs, and a random grid of Deep Neural Nets. In some cases, there will not be enough time to complete all the algorithms, so some may be missing from the leaderboard. AutoML then trains two Stacked Ensemble models.

h2o-ops commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-7541 Assignee: Tomas Fryda Reporter: Erin LeDell State: Resolved Fix Version: 3.32.0.1 Attachments: N/A Development PRs: Available

Linked PRs from JIRA

https://github.com/h2oai/h2o-3/pull/5006