Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
-
Updated
Aug 27, 2020 - C++
{{ message }}
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Python for《Deep Learning》,该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
Deep Learning API and Server in C++11 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
A library for debugging/inspecting machine learning classifiers and explaining their predictions
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference
Provide an input CSV and a target field to predict, generate a model + code to run it.
I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.
Of course, piping is a solution, but not for development in Jupyter Notebook, for example.
[UNMAINTAINED] Automated machine learning for analytics & production
A collection of research papers on decision, classification and regression trees with implementations.
MLBox is a powerful Automated Machine Learning python library.
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
A curated list of gradient boosting research papers with implementations.
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
REST web service for the true real-time scoring (<1 ms) of R, Scikit-Learn and Apache Spark models
I think it would be interesting to add a feature to export the best model with a scikit-learn wrapper. This would allow integrating the best AutoML model into a scikit-learn workflow.
I think most of the models that AutoML uses are already from scikit-learn, and those who aren't do provide scikit-learn wrappers, so I think it would be easy to implement.
Is there anything that makes this feature
H2O.ai Machine Learning Interpretability Resources
AI比赛相关信息汇总
Open solution to the Home Credit Default Risk challenge
Real time eye tracking for embedded and mobile devices.
Tiny Gradient Boosting Tree
Pure Java implementation of XGBoost predictor for online prediction tasks.
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Machine learning models for time series analysis
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Add a description, image, and links to the xgboost topic page so that developers can more easily learn about it.
To associate your repository with the xgboost topic, visit your repo's landing page and select "manage topics."
Support DataFrame.select_dtypes