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it exposes its functionality through a scikit-learn-like API [2] in idiomatic Scala using typeclasses
How does it compare to existing solutions?
doddle-model takes the position of scikit-learn in Scala and as a consequence, it's much more lightweight than e.g. Spark ML. Fitted models can be deployed anywhere, from simple applications to concurrent, distributed systems built with Akka, Apache Beam or a framework of your choice. Training of estimators happens in-memory, which is advantageous unless you are dealing with enormous datasets that absolutely cannot fit into RAM.
Installation
The project is published for Scala versions 2.11, 2.12 and 2.13. Add the dependency to your SBT project definition:
libraryDependencies ++=Seq(
"io.github.picnicml"%%"doddle-model"%"<latest_version>",
// add optionally to utilize native libraries for a significant performance boost"org.scalanlp"%%"breeze-natives"%"1.0"
)
Note that the latest version is displayed in the Latest Release badge above and that the v prefix should be removed from the SBT definition.
Want to help us? 🙌 We have a document that will make deciding how to do that much easier.
Performance
Performance of implementations is described here. Also, take a peek at what's written in that document if you encounter java.lang.OutOfMemoryError: Java heap space.
Core Maintainers
This is a collaborative project which wouldn't be possible without all the awesome contributors. The core team currently consists of the following developers: