Question 1

How do I install doddle-model in my Scala project?

Accepted Answer

Add the dependency to your SBT build file with 'io.github.picnicml' %% 'doddle-model' and optionally include 'breeze-natives' for performance. Remove the 'v' prefix from the version shown in the badges.

Question 2

doddle-model vs Spark ML: which should I use?

Accepted Answer

Use doddle-model for lightweight, in-memory ML with Scala immutability and scikit-learn API; choose Spark ML for distributed big data processing that doesn't fit into RAM or requires out-of-core capabilities.

Question 3

Can doddle-model handle deep learning or neural networks?

Accepted Answer

No, doddle-model focuses on traditional machine learning algorithms and does not support deep learning. For that, you'd need Scala bindings for TensorFlow or PyTorch, or switch to Python ecosystems.

Question 4

What happens if my dataset is too large for doddle-model?

Accepted Answer

You may encounter java.lang.OutOfMemoryError, as training requires all data in RAM. The performance wiki suggests using smaller datasets or alternative frameworks like Spark ML for out-of-memory processing.

Question 5

How does doddle-model compare to scikit-learn in performance?

Accepted Answer

Performance is comparable for in-memory tasks with the Breeze backend, but doddle-model is Scala-based, so it integrates better with JVM systems, though it lacks scikit-learn's extensive Python ecosystem and optimizations.

Question 6

Is doddle-model compatible with Apache Beam or Akka?

Accepted Answer

Yes, fitted models can be deployed in such frameworks, and the immutable estimators are designed for safe use in parallel and distributed environments, making it a good fit for systems built with Akka or Apache Beam.

doddle-model

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