Hi, The roadmap for the 1.1 release and MLLib includes algorithms such as:
Non-negative matrix factorization, Sparse SVD, Multiclass decision tree, Random Forests (?) and optimizers such as: ADMM, Accelerated gradient methods also a statistical toolbox that includes: descriptive statistics, sampling, hypothesis testing and hopefully Parallel model training for autotuning. Source: https://databricks-training.s3.amazonaws.com/slides/Spark_Summit_MLlib_070214_v2.pdf Best, Burak ----- Original Message ----- From: "Michael Malak" <michaelma...@yahoo.com.INVALID> To: dev@spark.apache.org Sent: Wednesday, July 9, 2014 11:43:26 AM Subject: 15 new MLlib algorithms At Spark Summit, Patrick Wendell indicated the number of MLlib algorithms would "roughly double" in 1.1 from the current approx. 15. http://spark-summit.org/wp-content/uploads/2014/07/Future-of-Spark-Patrick-Wendell.pdf What are the planned additional algorithms? In Jira, I only see two when filtering on version 1.1, component MLlib: one on multi-label and another on high dimensionality. https://issues.apache.org/jira/browse/SPARK-2329?jql=issuetype%20in%20(Brainstorming%2C%20Epic%2C%20%22New%20Feature%22%2C%20Story)%20AND%20fixVersion%20%3D%201.1.0%20AND%20component%20%3D%20MLlib http://tinyurl.com/ku7sehu
