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 Integrating friedrich into linfa nestordemeure/friedrich#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 💯