d20559b157
* Add GradientBoostedTrees Python examples to ML guide * I ran these in the pyspark shell, and they worked. * Add save/load to examples in ML guide * Added note to python docs about predict,transform not working within RDD actions,transformations in some cases (See SPARK-5981) CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #4750 from jkbradley/SPARK-5974 and squashes the following commits: c410e38 [Joseph K. Bradley] Added note to LabeledPoint about attributes bcae18b [Joseph K. Bradley] Added import of models for save/load examples in ml guide. Fixed line length for tree.py, feature.py (but not other ML Pyspark files yet). 6d81c3e [Joseph K. Bradley] completed python GBT examples 9903309 [Joseph K. Bradley] Added note to python docs about predict,transform not working within RDD actions,transformations in some cases c7dfad8 [Joseph K. Bradley] Added model save/load to ML guide. Added GBT examples to ML guide
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 - MLlib
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displayTitle: <a href="mllib-guide.html">MLlib</a> - Classification and Regression
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---
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MLlib 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>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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* [binary classification (SVMs, logistic regression)](mllib-linear-methods.html#binary-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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