Description
This is an umbrella JIRA to track MLlib wrappers in SparkR for the release 2.1. The wrappers should follow existing implementations such as `spark.glm`, `spark.kmeans`, `spark.naiveBayes`, etc. And the APIs should follow SPARK-14831:
spark.algo(data, formula, required params, [optional params]) summary(object) predict(object, newData) write.ml(object, path, ...)
Implementation should also update `read.ml` to load the saved models back. Documentation should follow SPARK-16090 and group the methods above into a single Rd.
Attachments
1.
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ALS wrapper in SparkR | Resolved | Junyang Qian | |
2.
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Isotonic Regression wrapper in SparkR | Resolved | Miao Wang | |
3.
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Multilayer Perceptron Classifier wrapper in SparkR | Resolved | Xin Ren | |
4.
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Random Forest wrapper in SparkR | Resolved | Felix Cheung | |
5.
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Gaussian Mixture Model wrapper in SparkR | Resolved | Yanbo Liang | |
6.
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LDA wrapper in SparkR | Resolved | Xusen Yin | |
7.
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Add multiclass logistic regression SparkR Wrapper | Resolved | Miao Wang | |
8.
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Gradient Boosted Tree wrapper in SparkR | Resolved | Felix Cheung |