Projects that are tagged with tree.


Logo JMLR MLPACK 2.1.0

by rcurtin - November 1, 2016, 16:01:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 69282 views, 12325 downloads, 6 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:

Fixed CoverTree to properly handle single-point datasets. - Fixed a bug in CosineTree (and thus QUIC-SVD) that caused split failures for some datasets (#717). - Added mlpack_preprocess_describe program, which can be used to print statistics on a given dataset (#742). - Fix prioritized recursion for k-furthest-neighbor search (mlpack_kfn and the KFN class), leading to orders-of-magnitude speedups in some cases. - Bump minimum required version of Armadillo to 4.200.0. - Added simple Gradient Descent optimizer, found in src/mlpack/core/optimizers/gradient_descent/ (#792). - Added approximate furthest neighbor search algorithms QDAFN and DrusillaSelect in src/mlpack/methods/approx_kfn/, with command-line program mlpack_approx_kfn.


Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 15553 views, 2753 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:
  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version