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Preview: http://54.82.240.23:4000/mllib-guide.html Table of contents: * Basics * Data types * Summary statistics * Classification and regression * linear support vector machine (SVM) * logistic regression * linear linear squares, Lasso, and ridge regression * decision tree * naive Bayes * Collaborative Filtering * alternating least squares (ALS) * Clustering * k-means * Dimensionality reduction * singular value decomposition (SVD) * principal component analysis (PCA) * Optimization * stochastic gradient descent * limited-memory BFGS (L-BFGS) Author: Xiangrui Meng <meng@databricks.com> Closes #422 from mengxr/mllib-doc and squashes the following commits: 944e3a9 [Xiangrui Meng] merge master f9fda28 [Xiangrui Meng] minor 9474065 [Xiangrui Meng] add alpha to ALS examples 928e630 [Xiangrui Meng] initialization_mode -> initializationMode 5bbff49 [Xiangrui Meng] add imports to labeled point examples c17440d [Xiangrui Meng] fix python nb example 28f40dc [Xiangrui Meng] remove localhost:4000 369a4d3 [Xiangrui Meng] Merge branch 'master' into mllib-doc 7dc95cc [Xiangrui Meng] update linear methods 053ad8a [Xiangrui Meng] add links to go back to the main page abbbf7e [Xiangrui Meng] update ALS argument names 648283e [Xiangrui Meng] level down statistics 14e2287 [Xiangrui Meng] add sample libsvm data and use it in guide 8cd2441 [Xiangrui Meng] minor updates 186ab07 [Xiangrui Meng] update section names 6568d65 [Xiangrui Meng] update toc, level up lr and svm 162ee12 [Xiangrui Meng] rename section names 5c1e1b1 [Xiangrui Meng] minor 8aeaba1 [Xiangrui Meng] wrap long lines 6ce6a6f [Xiangrui Meng] add summary statistics to toc 5760045 [Xiangrui Meng] claim beta cc604bf [Xiangrui Meng] remove classification and regression 92747b3 [Xiangrui Meng] make section titles consistent e605dd6 [Xiangrui Meng] add LIBSVM loader f639674 [Xiangrui Meng] add python section to migration guide c82ffb4 [Xiangrui Meng] clean optimization 31660eb [Xiangrui Meng] update linear algebra and stat 0a40837 [Xiangrui Meng] first pass over linear methods 1fc8271 [Xiangrui Meng] update toc 906ed0a [Xiangrui Meng] add a python example to naive bayes 5f0a700 [Xiangrui Meng] update collaborative filtering 656d416 [Xiangrui Meng] update mllib-clustering 86e143a [Xiangrui Meng] remove data types section from main page 8d1a128 [Xiangrui Meng] move part of linear algebra to data types and add Java/Python examples d1b5cbf [Xiangrui Meng] merge master 72e4804 [Xiangrui Meng] one pass over tree guide 64f8995 [Xiangrui Meng] move decision tree guide to a separate file 9fca001 [Xiangrui Meng] add first version of linear algebra guide 53c9552 [Xiangrui Meng] update dependencies f316ec2 [Xiangrui Meng] add migration guide f399f6c [Xiangrui Meng] move linear-algebra to dimensionality-reduction 182460f [Xiangrui Meng] add guide for naive Bayes 137fd1d [Xiangrui Meng] re-organize toc a61e434 [Xiangrui Meng] update mllib's toc
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| global | <a href="mllib-guide.html">MLlib</a> - Naive Bayes |
Naive Bayes is a simple multiclass classification algorithm with the assumption of independence between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to the training data, it computes the conditional probability distribution of each feature given label, and then it applies Bayes' theorem to compute the conditional probability distribution of label given an observation and use it for prediction. For more details, please visit the wikipedia page Naive Bayes classifier.
In MLlib, we implemented multinomial naive Bayes, which is typically used for document
classification. Within that context, each observation is a document, each feature represents a term,
whose value is the frequency of the term. For its formulation, please visit the wikipedia page
Multinomial naive Bayes
or the section
Naive Bayes text classification
from the book Introduction to Information
Retrieval. Additive smoothing can be used by
setting the parameter \lambda (default to $1.0$). For document classification, the input feature
vectors are usually sparse. Please supply sparse vectors as input to take advantage of
sparsity. Since the training data is only used once, it is not necessary to cache it.
Examples
NaiveBayes implements
multinomial naive Bayes. It takes an RDD of
LabeledPoint and an optional
smoothing parameter lambda as input, and output a
NaiveBayesModel, which
can be used for evaluation and prediction.
{% highlight scala %} import org.apache.spark.mllib.classification.NaiveBayes
val training: RDD[LabeledPoint] = ... // training set val test: RDD[LabeledPoint] = ... // test set
val model = NaiveBayes.train(training, lambda = 1.0) val prediction = model.predict(test.map(_.features))
val predictionAndLabel = prediction.zip(test.map(_.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() {% endhighlight %}
NaiveBayes implements
multinomial naive Bayes. It takes a Scala RDD of
LabeledPoint and an
optionally smoothing parameter lambda as input, and output a
NaiveBayesModel, which
can be used for evaluation and prediction.
{% highlight java %} import org.apache.spark.mllib.classification.NaiveBayes;
JavaRDD training = ... // training set JavaRDD test = ... // test set
NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
JavaRDD prediction = model.predict(test.map(new Function<LabeledPoint, Vector>() { public Vector call(LabeledPoint p) { return p.features(); } }) JavaPairRDD<Double, Double> predictionAndLabel = prediction.zip(test.map(new Function<LabeledPoint, Double>() { public Double call(LabeledPoint p) { return p.label(); } }) double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { public Boolean call(Tuple2<Double, Double> pl) { return pl._1() == pl._2(); } }).count() / test.count() {% endhighlight %}
NaiveBayes implements multinomial
naive Bayes. It takes an RDD of
LabeledPoint and an optionally
smoothing parameter lambda as input, and output a
NaiveBayesModel, which can be
used for evaluation and prediction.
{% highlight python %} from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import NaiveBayes
an RDD of LabeledPoint
data = sc.parallelize([ LabeledPoint(0.0, [0.0, 0.0]) ... # more labeled points ])
Train a naive Bayes model.
model = NaiveBayes.train(data, 1.0)
Make prediction.
prediction = model.predict([0.0, 0.0]) {% endhighlight %}