scikit-learn-intelex
xgb_vs_lightgbm
| scikit-learn-intelex | xgb_vs_lightgbm | |
|---|---|---|
| 3 | 2 | |
| 1,161 | 0 | |
| 1.1% | - | |
| 9.5 | 3.2 | |
| 3 days ago | over 2 years ago | |
| Python | R | |
| Apache License 2.0 | - |
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scikit-learn-intelex
- Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
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Improving xgb prediction times on a single core
I can recommend https://github.com/intel/scikit-learn-intelex. We have been using this and it works great. The prediction time is greatly reduced and it has been running very stable. It's super easy to install and convert the trained XGB models to this Intel format.
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Intel Extension for Scikit-Learn
Looks like they are responding to https://github.com/intel/scikit-learn-intelex#-acceleration
I completely agree. I hope some Intel competitor funds a scikit-learn developer to read this code and extract all the portable performance improvements.
xgb_vs_lightgbm
What are some alternatives?
cuml - cuML - RAPIDS Machine Learning Library
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
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eland - Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
scikit-learn - scikit-learn: machine learning in Python
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
oneDAL - oneAPI Data Analytics Library (oneDAL)
zillion - Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/
visualisa - A machine learning algorithm to recreate your images.
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.