[SPARK-15163] Mark experimental algorithms experimental in PySpark

• Type: Improvement
• Status: Resolved
• Priority: Trivial
• Resolution: Fixed
• Fix Version/s: 2.0.0
• Component/s: ML, PySpark
• Assignee: Holden Karau
• Reporter: Holden Karau
• Created: 05/May/16 20:20
• Updated: 04/Jan/17 00:07
• Resolved: 04/Jan/17 00:07

Description

While we are going through them anyways might as well mark the PySpark algorithm as experimental that are marked so in Scala

Issue Links

• is required by: SPARK-14813 ML 2.0 QA: API: Python API coverage
• is required by: SPARK-16486 Python API parity issues from 2.0 QA
• relates to: SPARK-14812 ML, Graph 2.0 QA: API: Experimental, DeveloperApi, final, sealed audit

Sub-Tasks

• Mark classification algorithms as experimental where marked so in scala: Resolved