754f820035
## What changes were proposed in this pull request? Add AL2 license to metadata of all .md files. This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing. ## How was this patch tested? Doc build Closes #24243 from srowen/SPARK-26918. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
63 lines
3.5 KiB
Markdown
63 lines
3.5 KiB
Markdown
---
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layout: global
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title: "MLlib: RDD-based API"
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displayTitle: "MLlib: RDD-based API"
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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This page documents sections of the MLlib guide for the RDD-based API (the `spark.mllib` package).
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Please see the [MLlib Main Guide](ml-guide.html) for the DataFrame-based API (the `spark.ml` package),
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which is now the primary API for MLlib.
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* [Data types](mllib-data-types.html)
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* [Basic statistics](mllib-statistics.html)
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* [summary statistics](mllib-statistics.html#summary-statistics)
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* [correlations](mllib-statistics.html#correlations)
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* [stratified sampling](mllib-statistics.html#stratified-sampling)
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* [hypothesis testing](mllib-statistics.html#hypothesis-testing)
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* [streaming significance testing](mllib-statistics.html#streaming-significance-testing)
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* [random data generation](mllib-statistics.html#random-data-generation)
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* [Classification and regression](mllib-classification-regression.html)
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* [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
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* [naive Bayes](mllib-naive-bayes.html)
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* [decision trees](mllib-decision-tree.html)
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* [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
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* [isotonic regression](mllib-isotonic-regression.html)
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* [Collaborative filtering](mllib-collaborative-filtering.html)
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* [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
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* [Clustering](mllib-clustering.html)
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* [k-means](mllib-clustering.html#k-means)
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* [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
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* [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic)
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* [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
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* [bisecting k-means](mllib-clustering.html#bisecting-kmeans)
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* [streaming k-means](mllib-clustering.html#streaming-k-means)
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* [Dimensionality reduction](mllib-dimensionality-reduction.html)
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* [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
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* [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
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* [Feature extraction and transformation](mllib-feature-extraction.html)
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* [Frequent pattern mining](mllib-frequent-pattern-mining.html)
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* [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
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* [association rules](mllib-frequent-pattern-mining.html#association-rules)
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* [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
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* [Evaluation metrics](mllib-evaluation-metrics.html)
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* [PMML model export](mllib-pmml-model-export.html)
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* [Optimization (developer)](mllib-optimization.html)
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* [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
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* [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
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