0001 ---
0002 layout: global
0003 title: "MLlib: RDD-based API"
0004 displayTitle: "MLlib: 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 This page documents sections of the MLlib guide for the RDD-based API (the `spark.mllib` package).
0023 Please see the [MLlib Main Guide](ml-guide.html) for the DataFrame-based API (the `spark.ml` package),
0024 which is now the primary API for MLlib.
0025
0026 * [Data types](mllib-data-types.html)
0027 * [Basic statistics](mllib-statistics.html)
0028 * [summary statistics](mllib-statistics.html#summary-statistics)
0029 * [correlations](mllib-statistics.html#correlations)
0030 * [stratified sampling](mllib-statistics.html#stratified-sampling)
0031 * [hypothesis testing](mllib-statistics.html#hypothesis-testing)
0032 * [streaming significance testing](mllib-statistics.html#streaming-significance-testing)
0033 * [random data generation](mllib-statistics.html#random-data-generation)
0034 * [Classification and regression](mllib-classification-regression.html)
0035 * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
0036 * [naive Bayes](mllib-naive-bayes.html)
0037 * [decision trees](mllib-decision-tree.html)
0038 * [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
0039 * [isotonic regression](mllib-isotonic-regression.html)
0040 * [Collaborative filtering](mllib-collaborative-filtering.html)
0041 * [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
0042 * [Clustering](mllib-clustering.html)
0043 * [k-means](mllib-clustering.html#k-means)
0044 * [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
0045 * [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic)
0046 * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
0047 * [bisecting k-means](mllib-clustering.html#bisecting-kmeans)
0048 * [streaming k-means](mllib-clustering.html#streaming-k-means)
0049 * [Dimensionality reduction](mllib-dimensionality-reduction.html)
0050 * [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
0051 * [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
0052 * [Feature extraction and transformation](mllib-feature-extraction.html)
0053 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
0054 * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
0055 * [association rules](mllib-frequent-pattern-mining.html#association-rules)
0056 * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
0057 * [Evaluation metrics](mllib-evaluation-metrics.html)
0058 * [PMML model export](mllib-pmml-model-export.html)
0059 * [Optimization (developer)](mllib-optimization.html)
0060 * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
0061 * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
0062
