Roadmap

In terms of functionality, the mid-term end goal is to achieve an offering of ML algorithms and pre-processing routines comparable to what is currently available in Python's scikit-learn.

These algorithms can either be:

• re-implemented in Rust;
• re-exported from an existing Rust crate, if available on crates.io with a compatible interface.

In no particular order, focusing on the main gaps:

• Clustering:

  • DBSCAN
  • Spectral clustering;
  • Hierarchical clustering;
  • OPTICS.

• Preprocessing:

  • PCA
  • ICA
  • Normalisation
  • CountVectoriser
  • TFIDF
  • t-SNE

• Supervised Learning:

  • Linear regression;
  • Ridge regression;
  • LASSO;
  • ElasticNet;
  • Support vector machines;
  • Nearest Neighbours;
  • Gaussian processes; (integrating friedrich - tracking issue https://github.com/nestordemeure/friedrich/issues/1)
  • Decision trees;
  • Random Forest
  • Naive Bayes
  • Logistic Regression
  • Ensemble Learning
  • Least Angle Regression
  • PLS

The collection is on purpose loose and non-exhaustive, it will evolve over time - if there is an ML algorithm that you find yourself using often on a day to day, please feel free to contribute it 💯

No source files, tests, or entry points are named. Start by reviewing the unchecked Gaussian processes item and its linked friedrich tracking issue, then compare the repository's existing algorithm coverage with the roadmap. Done means implementing or integrating a missing algorithm and updating the roadmap, but the specific scope is not defined.