Displaying 1 to 11 from 11 results

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++

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.

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more

  •    C++

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.

scoruby - Ruby Scoring API for PMML

  •    Ruby

Ruby scoring API for Predictive Model Markup Language (PMML).Currently supports Decision Tree, Random Forest Naive Bayes and Gradient Boosted Models.

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

  •    Jupyter

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.


  •    C++

[InitalGuess] Label Weight Index0:Value0 Index1:Value1 .. Each line contains an instance and is ended by a ‘\n’ character. Inital guess is optional. For two-class classification, Label is -1 or 1. For regression, Label is the target value, which can be any real number. Feature Index starts from 0. Feature Value can be any real number.

GBM-perf - Performance of various open source GBM implementations

  •    R

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.

GBM-tune - Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

  •    HTML

The goal of this repo is to study the impact of having one dataset/sample ("the dataset") when training and tuning machine learning models in practice (or in competitions) on the prediction accuracy on new data (that usually comes from a slightly different distribution due to non-stationarity). To keep things simple we focus on binary classification, use only one source dataset with mix of numeric and categorical features and no missing values, we don't perform feature engineering, tune only GBMs with lightgbm and random hyperparameter search (might also ensemble the best models later), and we use only AUC as a measure of accuracy.

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.