| --- |
| layout: global |
| title: "MLlib: RDD-based API" |
| displayTitle: "MLlib: RDD-based API" |
| license: | |
| Licensed to the Apache Software Foundation (ASF) under one or more |
| contributor license agreements. See the NOTICE file distributed with |
| this work for additional information regarding copyright ownership. |
| The ASF licenses this file to You under the Apache License, Version 2.0 |
| (the "License"); you may not use this file except in compliance with |
| the License. You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| --- |
| |
| This page documents sections of the MLlib guide for the RDD-based API (the `spark.mllib` package). |
| Please see the [MLlib Main Guide](ml-guide.html) for the DataFrame-based API (the `spark.ml` package), |
| which is now the primary API for MLlib. |
| |
| * [Data types](mllib-data-types.html) |
| * [Basic statistics](mllib-statistics.html) |
| * [summary statistics](mllib-statistics.html#summary-statistics) |
| * [correlations](mllib-statistics.html#correlations) |
| * [stratified sampling](mllib-statistics.html#stratified-sampling) |
| * [hypothesis testing](mllib-statistics.html#hypothesis-testing) |
| * [streaming significance testing](mllib-statistics.html#streaming-significance-testing) |
| * [random data generation](mllib-statistics.html#random-data-generation) |
| * [Classification and regression](mllib-classification-regression.html) |
| * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html) |
| * [naive Bayes](mllib-naive-bayes.html) |
| * [decision trees](mllib-decision-tree.html) |
| * [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html) |
| * [isotonic regression](mllib-isotonic-regression.html) |
| * [Collaborative filtering](mllib-collaborative-filtering.html) |
| * [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering) |
| * [Clustering](mllib-clustering.html) |
| * [k-means](mllib-clustering.html#k-means) |
| * [Gaussian mixture](mllib-clustering.html#gaussian-mixture) |
| * [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic) |
| * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda) |
| * [bisecting k-means](mllib-clustering.html#bisecting-k-means) |
| * [streaming k-means](mllib-clustering.html#streaming-k-means) |
| * [Dimensionality reduction](mllib-dimensionality-reduction.html) |
| * [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd) |
| * [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca) |
| * [Feature extraction and transformation](mllib-feature-extraction.html) |
| * [Frequent pattern mining](mllib-frequent-pattern-mining.html) |
| * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth) |
| * [association rules](mllib-frequent-pattern-mining.html#association-rules) |
| * [PrefixSpan](mllib-frequent-pattern-mining.html#prefixspan) |
| * [Evaluation metrics](mllib-evaluation-metrics.html) |
| * [PMML model export](mllib-pmml-model-export.html) |
| * [Optimization (developer)](mllib-optimization.html) |
| * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd) |
| * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs) |
