Displaying 1 to 9 from 9 results

mlens - ML-Ensemble – high performance ensemble learning

  •    Python

ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible. ML-Ensemble is thread safe as long as base learners are and can fall back on memory mapped multiprocessing for memory-neutral process-based concurrency. For tutorials and full documentation, visit the project website.

irf - Incremental Random Forest

  •    C++

The forest is maintained incrementally as samples are added or removed - rather than fully rebuilt from scratch every time - to save effort. It is not a streaming implementation, all the samples are stored and will be reseen when required to recursively rebuild invalidated subtrees. The effort to update each individual tree can vary substantially but the overall effort to update the forest is averaged across the trees so tends not to vary so much.

GENESIM - An innovative technique that constructs an ensemble of decision trees and converts this ensemble into a single, interpretable decision tree with an enhanced predictive performance

  •    Scilab

A wrapper is written around Orange C4.5, sklearn CART, GUIDE and QUEST. The returned object is a Decision Tree, which can be found in decisiontree.py. Moreover, different methods are available on this decision tree: classify new, unknown samples; visualise the tree; export it to string, JSON and DOT; etc. A wrapper written around the R package inTrees and an implementation of ISM can be found in the constructors package.

SnapshotEnsemble - Snapshot Ensembles in Torch (Snapshot Ensembles: Train 1, Get M for Free)

  •    Lua

This repository contains the Torch code for the paper Snapshot Ensembles: Train 1, Get M for Free. The code is based on fb.resnet.torch by Facebook .

subsemble - subsemble R package for ensemble learning

  •    R

The subsemble package is an R implementation of the Subsemble algorithm. Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. Stephanie Sapp, Mark J. van der Laan & John Canny. Subsemble: An ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6):1247-1259, 2014.

AdaptiveRandomForest - Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

  •    Java

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

xgboost-node - Run XGBoost model and make predictions in Node.js

  •    Cuda

XGBoost-Node is a Node.js interface of XGBoost. XGBoost is a library from DMLC. It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction. The package is made to run existing XGBoost model with Node.js easily.