Revision fdea642dbd17d74c8bf136c1746159acaa937d25 authored by donnyzone on 18 August 2017, 05:37:32 UTC, committed by gatorsmile on 18 August 2017, 05:37:41 UTC
## What changes were proposed in this pull request? https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21739 This issue is caused by introducing TimeZoneAwareExpression. When the **Cast** expression converts something into TimestampType, it should be resolved with setting `timezoneId`. In general, it is resolved in LogicalPlan phase. However, there are still some places that use Cast expression statically to convert datatypes without setting `timezoneId`. In such cases, `NoSuchElementException: None.get` will be thrown for TimestampType. This PR is proposed to fix the issue. We have checked the whole project and found two such usages(i.e., in`TableReader` and `HiveTableScanExec`). ## How was this patch tested? unit test Author: donnyzone <wellfengzhu@gmail.com> Closes #18960 from DonnyZone/spark-21739. (cherry picked from commit 310454be3b0ce5ff6b6ef0070c5daadf6fb16927) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
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mllib-classification-regression.md
---
layout: global
title: Classification and Regression - RDD-based API
displayTitle: Classification and Regression - RDD-based API
---
The `spark.mllib` package supports various methods for
[binary classification](http://en.wikipedia.org/wiki/Binary_classification),
[multiclass
classification](http://en.wikipedia.org/wiki/Multiclass_classification), and
[regression analysis](http://en.wikipedia.org/wiki/Regression_analysis). The table below outlines
the supported algorithms for each type of problem.
<table class="table">
<thead>
<tr><th>Problem Type</th><th>Supported Methods</th></tr>
</thead>
<tbody>
<tr>
<td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td>
</tr>
<tr>
<td>Multiclass Classification</td><td>logistic regression, decision trees, random forests, naive Bayes</td>
</tr>
<tr>
<td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td>
</tr>
</tbody>
</table>
More details for these methods can be found here:
* [Linear models](mllib-linear-methods.html)
* [classification (SVMs, logistic regression)](mllib-linear-methods.html#classification)
* [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
* [Decision trees](mllib-decision-tree.html)
* [Ensembles of decision trees](mllib-ensembles.html)
* [random forests](mllib-ensembles.html#random-forests)
* [gradient-boosted trees](mllib-ensembles.html#gradient-boosted-trees-gbts)
* [Naive Bayes](mllib-naive-bayes.html)
* [Isotonic regression](mllib-isotonic-regression.html)
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