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tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.
machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programmingbenchm-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
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 sparkMachine-Learning-with-Python - Python codes for common Machine Learning Algorithms
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-algorithmuseR-machine-learning-tutorial - useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016
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 rscoruby - Ruby Scoring API for PMML
Ruby scoring API for Predictive Model Markup Language (PMML).Currently supports Decision Tree, Random Forest Naive Bayes and Gradient Boosted Models.
ruby-gem pmml random-forest classification rubyml machine-learning gradient-boosting-classifier gbm gradient-boosted-models decision-tree naive-bayesrandom-forest-classifier - A random forest classifier in Javascript.
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.Modeled after scikit-learn's RandomForestClassifier.
random-forest machine-learning classifierdecision-tree-js - Small JavaScript implementation of ID3 Decision tree
Small JavaScript implementation of algorithm for training Decision Tree and Random Forest classifiers.
decision-tree random-forest machine-learningedarf - exploratory data analysis using random forests
Functions useful for exploratory data analysis using random forests. This package extends the functionality of random forests fit by party (multivariate, regression, and classification), randomForestSRC (regression and classification,), randomForest (regression and classification), and ranger (classification and regression).
random-forest r rstats exploratory-data-analysis machine-learningreceiptdID - Receipt
Receipt.ID is a multi-label, multi-class, hierarchical classification system. It trains individual Random Forest text-based classifiers and combines the result with other features. Receipt.ID is built to scale with an application as the taxonomy for the domain in which it is applied grows. The data preprocessing code is provided in the notebook receiptID_1_Data_Preprocessing.ipynb. While the modeling code is provided in the notebook receiptID_2_Model.ipynb.
machine-learning random-forest word2veccl-random-forest - Random forest in Common Lisp
Cl-random-forest is a implementation of Random Forest for multiclass classification and univariate regression written in Common Lisp. It also includes a implementation of Global Refinement of Random Forest (Ren, Cao, Wei and Sun. “Global Refinement of Random Forest” CVPR2015). This refinement makes faster and more accurate than standard Random Forest. A dataset consists of a target vector and a input data matrix. For classification, the target vector should be a fixnum simple-vector and the data matrix should be a 2-dimensional double-float array whose row corresponds one datum. Note that the target is a integer starting from 0. For example, the following dataset is valid for 4-class classification with 2-dimensional input.
random-forest machine-learning common-lisp classifier regressionMachine-Learning-in-R - 4-hour tutorial on machine learning in R: knn, decision trees, random forest, boosting, superlearner
This is the repository for D-Lab’s Introduction to Machine Learning in R workshop.
machine-learning dlab-berkeley tutorial knn random-forest gradient-boosting-machine superlearner decision-treesinfiniteboost - InfiniteBoost: building infinite ensembles with gradient descent
InfiniteBoost is an approach to building ensembles which combines best sides of random forest and gradient boosting. Trees in the ensemble encounter mistakes done by previous trees (as in gradient boosting), but due to modified scheme of encountering contributions the ensemble converges to the limit, thus avoiding overfitting (just as random forest).
machine-learning research gradient-boosting random-forest experimentsemtrees - Tree-based machine learning classifiers for embedded systems
Tree-based machine learning classifiers for microcontroller and embedded systems. Train in Python, then do inference on any device with support for C.
machine-learning random-forest classifier embedded-systems microcontroller scikit-learnFlower-Recognition - Image recognition tool for flower clasification.
Application of computer vision techniques to get useful data for the machine learning algorithms in order to create a flower classifier. Made by Braulio Vargas López and Marta Gómez Macías for our university subject Computer Vision at University of Granada.
opencv image-recognition flowers random-forest svmVariantSpark - machine learning for genomic variants
Genomic variants interpretation toolkit
variant-spark gwas random-forest genome association-studies vcfETFMachineLearning - Implementation of a paper in q/KDB+ and python - "Forecasting ETFs with Machine Learning Algorithms" - Jim Kyung-Soo Liew and Boris Mayster
Please make sure you use the csv files inside the cvs_files/ directory and point to the right path inside your q code.
quantitative-trading neural-networks deep-learning random-forest support-vector-machines kdb kxFIFA-World-Cup-Prediction - Predict who will win the FIFA World Cup 2018
Data: Data are assembled from multiple sources, most of them are from Kaggle, others come from FIFA website / EA games. Feature list reflects those factors.
fifa worldcup2018 prediction random-forestAdaptiveRandomForest - Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04
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 ensemble2018-MachineLearning-Lectures-ESA - Machine Learning Lectures at the European Space Agency (ESA) in 2018
In 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).
machinelearning machine-learning linear-regression support-vector-machines decision-trees random-forest neural-network deep-learning clustering pca anomaly-detection text-mining tf-idf topic-modeling lectures lecture-slides lecture-material lecture-videosreproduce-stock-market-direction-random-forests - Reproduce research from paper "Predicting the direction of stock market prices using random forest"
This is my attemp to reproduce this paper. In my way I found that the results I got are much worse than those from the authors and I wonder if the authors accidentaly had a data leakage issue. Please, let me know if you notice any mistake in the analysis / code or if you feel there is something I misunderstood.
stock-market-prices random-forest paper reproducibility stock-market-prediction
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