LightGBM - A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks

•    C++

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For more details, please refer to Features.Experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.

gbdt gbm machine-learning data-mining kaggle efficiency distributed lightgbm gbrt

DMTK - Microsoft Distributed Machine Learning Toolkit

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

dmtk multiverso lightgbm microsoft machine-learning

auto_ml - Automated machine learning for analytics & production

•    Python

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auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligence

open-solution-home-credit - Open solution to the Home Credit Default Risk challenge :house_with_garden:

•    Python

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This is an open solution to the Home Credit Default Risk challenge 🏡. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.

machine-learning deep-learning kaggle pipeline feature-engineering reproducible-experiments reproducibility pipeline-framework lightgbm xgboost neptune competition credit-scoring credit-risk open-source python3 python35

fast_retraining - Show how to perform fast retraining with LightGBM in different business cases

•    Jupyter

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In this repo we compare two of the fastest boosted decision tree libraries: XGBoost and LightGBM. We will evaluate them across datasets of several domains and different sizes.On July 25, 2017, we published a blog post evaluating both libraries and discussing the benchmark results. The post is Lessons Learned From Benchmarking Fast Machine Learning Algorithms.

lightgbm xgboost boosted-trees machine-learning gpu benchmark azure distributed-systems gbdt gbm gbrt kaggle

Apartment-Interest-Prediction - Predict people interest in renting specific NYC apartments

•    Jupyter

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Predict people interest in renting specific apartments. The challenge combines structured data, geolocalization, time data, free text and images. This solution features Gradient Boosted Trees (XGBoost and LightGBM) and does not use stacking, due to lack of time.

kaggle kaggle-competition machine-learning geolocalization xgboost gradient-boosting lightgbm clustering natural-language-processing

Arch-Data-Science - Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

•    Shell

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Welcome to my repo to build Data Science, Machine Learning, Computer Vision, Natural language Processing and Deep Learning packages from source. My Data Science environment is running from a LXC container so Tensorflow build system, bazel, must be build with its auto-sandboxing disabled.

archlinux data-science machine-learning deep-learning package tensorflow scikit-learn mxnet opencv nervana pandas cudnn cuda pytorch spacy natural-language-processing natural-language-understanding xgboost lightgbm mkl

GBM-perf - Performance of various open source GBM implementations

•    R

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Performance of various open source GBM implementations (h2o, xgboost, lightgbm) on the airline dataset (1M and 10M records). If you don't have a GPU, lightgbm (CPU) trains the fastest.

machine-learning gradient-boosting-machine gbm h2oai xgboost lightgbm benchmark

open-solution-mapping-challenge - Open solution to the Mapping Challenge :earth_americas:

•    Python

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Poster that summarizs our project is available here. Open solution to the CrowdAI Mapping Challenge competition.

data-science machine-learning deep-learning kaggle satellite-imagery data-science-learning lightgbm unet unet-image-segmentation unet-pytorch neptune machine-learning-lab mapping-challenge crowdai competition pipeline pipeline-framework

open-solution-talking-data - Open solution to the TalkingData AdTracking Fraud Detection Challenge

•    Jupyter

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This is an open solution to the TalkingData Challenge. Deliver open source, ready-to-use and extendable solution to this competition. This solution should - by itself - establish solid benchmark, as well as provide good base for your custom ideas and experiments.

machine-learning data-science kaggle pipeline-framework talkingdata-challenge neptune deep-learning lightgbm light-gbm

open-solution-value-prediction - Open solution to the Santander Value Prediction Challenge :tropical_fish:

•    Python

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In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉. You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.

machine-learning deep-learning data-science reproducibility reproducible-experiments open-source education training lightgbm xgboost sklearn pipeline-framework ensemble-model neptune santander competition banking-applications

leaves - pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

•    Go

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leaves is a library implementing prediction code for GBRT (Gradient Boosting Regression Trees) models in pure Go. The goal of the project - make it possible to use models from popular GBRT frameworks in Go programs without C API bindings. In order to use XGBoost model, just change leaves.LGEnsembleFromFile, to leaves.XGEnsembleFromFile.

machine-learning lightgbm xgboost decision-trees boosting

jpmml-sparkml-lightgbm - JPMML-SparkML plugin for converting LightGBM-Spark models to PMML

•    Java

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JPMML-SparkML plugin for converting LightGBM-Spark models to PMML

pmml apache-spark sparkml lightgbm machine-learning

DC-hi_guides - [Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11

•    Python

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[Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11

data-mining xgboost lightgbm stacking

kaggle-recruit-restaurant - :trophy: Kaggle 8th place solution

•    Jupyter

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My solution ranked 8th out of 2216 on the Recruit Restaurant Visitor Forecasting Kaggle competition. The solution focuses on targeted feature engineering and LightGBM cross-validation.

kaggle lightgbm kaggle-recruit-restaurant timeseries