mlens - ML-Ensemble – high performance ensemble learning

•    Python

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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.

ensemble-learning machine-learning ensemble learners stacking stack ensembles

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

•    Python

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

irf - Incremental Random Forest

•    C++

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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.

incremental random forest machine learning native sparse classifier ensemble supervised

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

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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.

decision-trees data-mining ensemble

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

•    Lua

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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 .

deep-learning ensemble torch

subsemble - subsemble R package for ensemble learning

•    R

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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.

ensemble ensemble-learning cross-validation machine-learning machine-learning-algorithms r big-data

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

•    Java

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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.

moa datastream classification ensemble-learning random-forest concept-drift decision-trees ensemble

random-forest - Random forest for classification and regression.

•    Javascript

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Random forest for classification and regression.

random forest ensemble machine learning data mining datamining

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

•    Cuda

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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.

machine-learning nodejs xgboost boosting-algorithms gbdt algorithm classifier gbm ensemble kaggle kaggle-competition dmlc