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• Be widely known

• Be used and accepted (academic citations and concrete use cases can help justify this)

• Be highly scalable

• Be well documented

• Have APIs consistent with other algorithms in MLLib that accomplish the same thing

• Come with a reasonable expectation of developer support.

• Have `@Since` annotation on public classes, methods, and variables.

Code Review Criteria

Before considering how to contribute code, it's useful to understand how code is reviewed, and why changes may be rejected. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review.

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