Version 0.1#

Changelog#

  • Feature Implementation of the two-means Unsupervised Random Forest, by Adam Li (#9)

  • Feature Implementation of oblique Unsupervised Random Forest, by Adam Li (#11)

  • Feature Implementation of manifold oblique Random Forest, by Adam Li (#21)

  • Feature Implementation of fastBIC criterion for unsupervised tree models, by Adam Li and Jong Shin (#45)

  • Fix Fix a bug in Patch oblique random forest that samples outside the data boundaries and adds a user guide, by Adam Li (#61)

  • Feature MORF trees now can sample n-dimensional patches inside an n-dimensional structure sample and make any arbitrary axis discontinuous, by Adam Li (#63)

  • Feature All tree types can compute similarity and dissimilarity matrices, by Sambit Panda and Adam Li (#64)

  • Feature MORF trees now can normalize by feature weight per sample per feature column, by Adam Li (#67)

  • Feature A general-kernel MORF is now implemented where users can pass in a kernel library, by Adam Li (#70)

  • Feature Implementation of ObliqueDecisionTreeRegressor, PatchObliqueDecisionTreeRegressor, ObliqueRandomForestRegressor, PatchObliqueRandomForestRegressor, by SUKI-O (#72)

  • Feature Implementation of HonestTreeClassifier, HonestForestClassifier, by Sambit Panda, Adam Li, Ronan Perry and Haoyin Xu (#57)

  • Feature Implementation of (conditional) mutual information estimation via unsupervised tree models and added NearestNeighborsMetaEstimator by Adam Li (#83)

  • Feature Add multi-output support to HonestTreeClassifier, HonestForestClassifier, by Ronan Perry, Haoyin Xu and Adam Li (#86)

Code and Documentation Contributors#

Thanks to everyone who has contributed to the maintenance and improvement of the project since version inception, including: