At this time, you need to do one-vs-all manually for multiclass training. For your second question, if the algorithm is implemented in Java/Scala/Python and designed for single machine, you can broadcast the dataset to each worker, train models on workers. If the algorithm is implemented in a different language, maybe you need pipe to train the models outside JVM (similar to Hadoop Streaming). If the algorithm is designed for a different parallel platform, then it may be hard to use it in Spark. -Xiangrui
On Sat, Jun 7, 2014 at 7:15 AM, littlebird <cxp...@163.com> wrote: > Hi All, > As we know, In MLlib the SVM is used for binary classification. I wonder > how to train SVM model for mutiple classification in MLlib. In addition, how > to apply the machine learning algorithm in Spark if the algorithm isn't > included in MLlib. Thank you. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/How-to-process-multiple-classification-with-SVM-in-MLlib-tp7174.html > Sent from the Apache Spark User List mailing list archive at Nabble.com.
