useR-machine-learning-tutorial - useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016

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Instructions for how to install the necessary software for this tutorial is available here. Data for the tutorial can be downloaded by running ./data/get-data.sh (requires wget). Certain algorithms don't scale well when there are millions of features. For example, decision trees require computing some sort of metric (to determine the splits) on all the feature values (or some fraction of the values as in Random Forest and Stochastic GBM). Therefore, computation time is linear in the number of features. Other algorithms, such as GLM, scale much better to high-dimensional (n << p) and wide data with appropriate regularization (e.g. Lasso, Elastic Net, Ridge).

machine-learning deep-learning random-forest gradient-boosting-machine tutorial data-science ensemble-learning r

PythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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Practice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayes

Machine-Learning-with-Python - Python codes for common Machine Learning Algorithms

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Python codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithm

brushfire - Distributed decision tree ensemble learning in Scala

•    Scala

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Brushfire is a framework for distributed supervised learning of decision tree ensemble models in Scala.The basic approach to distributed tree learning is inspired by Google's PLANET, but considerably generalized thanks to Scala's type parameterization and Algebird's aggregation abstractions.

mlens - ML-Ensemble – high performance ensemble learning

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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

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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

elf-project

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The name stands for ensemble learning framework. It is a collection of machine learning algorithms for classification and regression with the possibility of connecting them together via ensemble learning. It is written in C++.

Apache Mahout - Scalable machine learning library

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Apache Mahout has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent pattern mining.

machine-learning classification data-mining fuzzy

DAT8 - General Assembly's 2015 Data Science course in Washington, DC

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Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).

data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaning

grt - gesture recognition toolkit

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The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition. Classification: Adaboost, Decision Tree, Dynamic Time Warping, Gaussian Mixture Models, Hidden Markov Models, k-nearest neighbor, Naive Bayes, Random Forests, Support Vector Machine, Softmax, and more...

gesture-recognition grt machine-learning gesture-recognition-toolkit support-vector-machine random-forest kmeans dynamic-time-warping softmax linear-regression

rumale - Rumale is a machine learning library in Ruby

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Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

machine-learning data-science data-analysis artificial-intelligence

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

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The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpeg

ranger - A Fast Implementation of Random Forests

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ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). Includes implementations of extremely randomized trees (Geurts et al. 2006) and quantile regression forests (Meinshausen 2006). ranger is written in C++, but a version for R is available, too. We recommend to use the R version. It is easy to install and use and the results are readily available for further analysis. The R version is as fast as the standalone C++ version.

Java Machine Learning Library

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Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

pico - Pixel Intensity Comparison-based Object detection

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The pico framework is a modifcation of the standard Viola-Jones method. The basic idea is to scan the image with a cascade of binary classifers at all reasonable positions and scales. An image region is classifed as an object of interest if it successfully passes all the members of the cascade. Each binary classifier consists of an ensemble of decision trees with pixel intensity comparisons as binary tests in their internal nodes. This enables the detector to process image regions at very high speed.

face-detection face detection objects image-analysis

interpret - Fit interpretable models. Explain blackbox machine learning.

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Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM)* which has both high accuracy and intelligibility. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. In addition to EBM, InterpretML also supports methods like LIME, SHAP, linear models, partial dependence, decision trees and rule lists. The package makes it easy to compare and contrast models to find the best one for your needs.

machine-learning interpretability gradient-boosting blackbox scikit-learn xai interpretml

OpenDT

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A fast decision tree program featuring distributed ensemble learning across multiple processors using MPI. Has been run on 1 cpu, small clusters, and the ASCI Blue supercomputer. Ensembles are often more accurate than their single classifier counterparts

catboost - CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

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CatBoost is a machine learning method based on gradient boosting over decision trees. All CatBoost documentation is available here.

machine-learning decision-trees gradient-boosting gbm gbdt r kaggle gpu-computing catboost tutorial categorical-features distributed gpu coreml opensource data-science big-data

benchm-ml - A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc

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This project aims at a minimal benchmark for scalability, speed and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications. Note: While a large part of this benchmark was done in Spring 2015 reflecting the state of ML implementations at that time, this repo is being updated if I see significant changes in implementations or new implementations have become widely available (e.g. lightgbm). Also, please find a summary of the progress and learnings from this benchmark at the end of this repo.

machine-learning data-science r gradient-boosting-machine random-forest deep-learning xgboost h2o spark

StatPatternRecognition

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The package implements a variety of tools for categorization of multivariate data such as boosted decision trees, bagging and random forest, bump hunting (PRIM), a multi-class learner and others.