Major 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.
|
ALS wrapper in SparkR |
|
Resolved | Junyang Qian |
2.
|
Isotonic Regression wrapper in SparkR |
|
Resolved | Miao Wang |
3.
|
Multilayer Perceptron Classifier wrapper in SparkR |
|
Resolved | Xin Ren |
4.
|
Random Forest wrapper in SparkR |
|
Resolved | Felix Cheung |
5.
|
Gaussian Mixture Model wrapper in SparkR |
|
Resolved | Yanbo Liang |
6.
|
LDA wrapper in SparkR |
|
Resolved | Xusen Yin |
7.
|
Add multiclass logistic regression SparkR Wrapper |
|
Resolved | Miao Wang |
8.
|
Gradient Boosted Tree wrapper in SparkR |
|
Resolved | Felix Cheung |