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| <h1 class="title">MLlib: RDD-based API</h1> |
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| <p>This page documents sections of the MLlib guide for the RDD-based API (the <code>spark.mllib</code> package). |
| Please see the <a href="ml-guide.html">MLlib Main Guide</a> for the DataFrame-based API (the <code>spark.ml</code> package), |
| which is now the primary API for MLlib.</p> |
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| <li><a href="mllib-data-types.html">Data types</a></li> |
| <li><a href="mllib-statistics.html">Basic statistics</a> |
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| <li><a href="mllib-statistics.html#summary-statistics">summary statistics</a></li> |
| <li><a href="mllib-statistics.html#correlations">correlations</a></li> |
| <li><a href="mllib-statistics.html#stratified-sampling">stratified sampling</a></li> |
| <li><a href="mllib-statistics.html#hypothesis-testing">hypothesis testing</a></li> |
| <li><a href="mllib-statistics.html#streaming-significance-testing">streaming significance testing</a></li> |
| <li><a href="mllib-statistics.html#random-data-generation">random data generation</a></li> |
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| <li><a href="mllib-classification-regression.html">Classification and regression</a> |
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| <li><a href="mllib-isotonic-regression.html">isotonic regression</a></li> |
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| <li><a href="mllib-collaborative-filtering.html">Collaborative filtering</a> |
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| <li><a href="mllib-collaborative-filtering.html#collaborative-filtering">alternating least squares (ALS)</a></li> |
| </ul> |
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| <li><a href="mllib-clustering.html">Clustering</a> |
| <ul> |
| <li><a href="mllib-clustering.html#k-means">k-means</a></li> |
| <li><a href="mllib-clustering.html#gaussian-mixture">Gaussian mixture</a></li> |
| <li><a href="mllib-clustering.html#power-iteration-clustering-pic">power iteration clustering (PIC)</a></li> |
| <li><a href="mllib-clustering.html#latent-dirichlet-allocation-lda">latent Dirichlet allocation (LDA)</a></li> |
| <li><a href="mllib-clustering.html#bisecting-kmeans">bisecting k-means</a></li> |
| <li><a href="mllib-clustering.html#streaming-k-means">streaming k-means</a></li> |
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| <li><a href="mllib-dimensionality-reduction.html">Dimensionality reduction</a> |
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| <li><a href="mllib-dimensionality-reduction.html#singular-value-decomposition-svd">singular value decomposition (SVD)</a></li> |
| <li><a href="mllib-dimensionality-reduction.html#principal-component-analysis-pca">principal component analysis (PCA)</a></li> |
| </ul> |
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| <li><a href="mllib-feature-extraction.html">Feature extraction and transformation</a></li> |
| <li><a href="mllib-frequent-pattern-mining.html">Frequent pattern mining</a> |
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| <li><a href="mllib-frequent-pattern-mining.html#fp-growth">FP-growth</a></li> |
| <li><a href="mllib-frequent-pattern-mining.html#association-rules">association rules</a></li> |
| <li><a href="mllib-frequent-pattern-mining.html#prefix-span">PrefixSpan</a></li> |
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| <li><a href="mllib-evaluation-metrics.html">Evaluation metrics</a></li> |
| <li><a href="mllib-pmml-model-export.html">PMML model export</a></li> |
| <li><a href="mllib-optimization.html">Optimization (developer)</a> |
| <ul> |
| <li><a href="mllib-optimization.html#stochastic-gradient-descent-sgd">stochastic gradient descent</a></li> |
| <li><a href="mllib-optimization.html#limited-memory-bfgs-l-bfgs">limited-memory BFGS (L-BFGS)</a></li> |
| </ul> |
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