0001 ---
0002 layout: global
0003 title: Classification and Regression - RDD-based API
0004 displayTitle: Classification and Regression - RDD-based API
0005 license: |
0006 Licensed to the Apache Software Foundation (ASF) under one or more
0007 contributor license agreements. See the NOTICE file distributed with
0008 this work for additional information regarding copyright ownership.
0009 The ASF licenses this file to You under the Apache License, Version 2.0
0010 (the "License"); you may not use this file except in compliance with
0011 the License. You may obtain a copy of the License at
0012
0013 http://www.apache.org/licenses/LICENSE-2.0
0014
0015 Unless required by applicable law or agreed to in writing, software
0016 distributed under the License is distributed on an "AS IS" BASIS,
0017 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0018 See the License for the specific language governing permissions and
0019 limitations under the License.
0020 ---
0021
0022 The `spark.mllib` package supports various methods for
0023 [binary classification](http://en.wikipedia.org/wiki/Binary_classification),
0024 [multiclass
0025 classification](http://en.wikipedia.org/wiki/Multiclass_classification), and
0026 [regression analysis](http://en.wikipedia.org/wiki/Regression_analysis). The table below outlines
0027 the supported algorithms for each type of problem.
0028
0029 <table class="table">
0030 <thead>
0031 <tr><th>Problem Type</th><th>Supported Methods</th></tr>
0032 </thead>
0033 <tbody>
0034 <tr>
0035 <td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td>
0036 </tr>
0037 <tr>
0038 <td>Multiclass Classification</td><td>logistic regression, decision trees, random forests, naive Bayes</td>
0039 </tr>
0040 <tr>
0041 <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td>
0042 </tr>
0043 </tbody>
0044 </table>
0045
0046 More details for these methods can be found here:
0047
0048 * [Linear models](mllib-linear-methods.html)
0049 * [classification (SVMs, logistic regression)](mllib-linear-methods.html#classification)
0050 * [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
0051 * [Decision trees](mllib-decision-tree.html)
0052 * [Ensembles of decision trees](mllib-ensembles.html)
0053 * [random forests](mllib-ensembles.html#random-forests)
0054 * [gradient-boosted trees](mllib-ensembles.html#gradient-boosted-trees-gbts)
0055 * [Naive Bayes](mllib-naive-bayes.html)
0056 * [Isotonic regression](mllib-isotonic-regression.html)
