d6dc12ef01
## What changes were proposed in this pull request? This PR continues the work in #11486 from yinxusen with some code refactoring. In R package e1071, `naiveBayes` supports both categorical (Bernoulli) and continuous features (Gaussian), while in MLlib we support Bernoulli and multinomial. This PR implements the common subset: Bernoulli. I moved the implementation out from SparkRWrappers to NaiveBayesWrapper to make it easier to read. Argument names, default values, and summary now match e1071's naiveBayes. I removed the preprocess part that omit NA values because we don't know which columns to process. ## How was this patch tested? Test against output from R package e1071's naiveBayes. cc: yanboliang yinxusen Closes #11486 Author: Xusen Yin <yinxusen@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #11890 from mengxr/SPARK-13449.
41 lines
709 B
Plaintext
41 lines
709 B
Plaintext
Package: SparkR
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Type: Package
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Title: R frontend for Spark
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Version: 2.0.0
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Date: 2013-09-09
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Author: The Apache Software Foundation
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Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
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Imports:
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methods
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Depends:
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R (>= 3.0),
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methods,
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Suggests:
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testthat,
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e1071
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Description: R frontend for Spark
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License: Apache License (== 2.0)
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Collate:
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'schema.R'
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'generics.R'
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'jobj.R'
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'column.R'
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'group.R'
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'RDD.R'
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'pairRDD.R'
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'DataFrame.R'
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'SQLContext.R'
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'backend.R'
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'broadcast.R'
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'client.R'
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'context.R'
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'deserialize.R'
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'functions.R'
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'mllib.R'
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'serialize.R'
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'sparkR.R'
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'stats.R'
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'types.R'
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'utils.R'
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RoxygenNote: 5.0.1
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