5ffd5d3838
## 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.
42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
---
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layout: global
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title: Classification and Regression - RDD-based API
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displayTitle: Classification and Regression - RDD-based API
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---
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The `spark.mllib` package supports various methods for
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[binary classification](http://en.wikipedia.org/wiki/Binary_classification),
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[multiclass
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classification](http://en.wikipedia.org/wiki/Multiclass_classification), and
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[regression analysis](http://en.wikipedia.org/wiki/Regression_analysis). The table below outlines
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the supported algorithms for each type of problem.
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<table class="table">
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<thead>
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<tr><th>Problem Type</th><th>Supported Methods</th></tr>
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</thead>
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<tbody>
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<tr>
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<td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td>
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</tr>
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<tr>
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<td>Multiclass Classification</td><td>logistic regression, decision trees, random forests, naive Bayes</td>
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</tr>
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<tr>
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<td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td>
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</tr>
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</tbody>
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</table>
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More details for these methods can be found here:
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* [Linear models](mllib-linear-methods.html)
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* [classification (SVMs, logistic regression)](mllib-linear-methods.html#classification)
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* [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
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* [Decision trees](mllib-decision-tree.html)
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* [Ensembles of decision trees](mllib-ensembles.html)
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* [random forests](mllib-ensembles.html#random-forests)
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* [gradient-boosted trees](mllib-ensembles.html#gradient-boosted-trees-gbts)
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* [Naive Bayes](mllib-naive-bayes.html)
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* [Isotonic regression](mllib-isotonic-regression.html)
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