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## What changes were proposed in this pull request? Made DataFrame-based API primary * Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html * mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html * ml-guide.html includes a "maintenance mode" announcement about the RDD-based API * **Reviewers: please check this carefully** * (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix * Moved migration guide to ml-guide from mllib-guide * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides * **Reviewers**: I did not change any of the content of the migration guides. Reorganized DataFrame-based guide: * ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc. * Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html * **Reviewers**: I did not change the content of these guides, except some intro text. * Sidebar remains the same, but with pipeline and tuning sections added Other: * ml-classification-regression.html: Moved text about linear methods to new section in page ## How was this patch tested? Generated docs locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #14213 from jkbradley/ml-guide-2.0.
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| layout | title | displayTitle |
|---|---|---|
| global | Classification and Regression - RDD-based API | Classification and Regression - RDD-based API |
The spark.mllib package supports various methods for
binary classification,
multiclass
classification, and
regression analysis. The table below outlines
the supported algorithms for each type of problem.
| Problem Type | Supported Methods |
|---|---|
| Binary Classification | linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes |
| Multiclass Classification | logistic regression, decision trees, random forests, naive Bayes |
| Regression | linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression |
More details for these methods can be found here: