adds lightgbm #2576
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adds lightgbm #2576
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The market cycle has not fully found its balance, but I'm confident trading with this platform, they understand the situation, their signals are bullish. All thanks to Glòrıãförextràdeş on Instagram. |
Joelokon
approved these changes
Apr 16, 2024
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Good |
Joelokon
approved these changes
Apr 18, 2024
narumiruna
approved these changes
Jun 22, 2024
narumiruna
reviewed
Jun 29, 2024
| @@ -703,6 +703,7 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). | |||
| * [vowpal_porpoise](https://github.com/josephreisinger/vowpal_porpoise) - A lightweight Python wrapper for [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/). | |||
| * [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library. | |||
| * [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries. | |||
| * [LightGBM](https://pypi.org/project/lightgbm/) - Provides a fast, distributed, and high-performance gradient boosting framework for large-scale machine learning. | |||
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[LightGBM](https://github.com/microsoft/LightGBM)
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What is this Python project?
LightGBM, short for Light Gradient Boosting Machine is ideal for large-scale data projects.
Efficient Large-Scale Learning: Optimized for performance, making it suitable for large datasets and high-dimensional data.
Fast Training: Implements novel techniques like gradient-based one-side sampling and exclusive feature bundling, speeding up training times.
Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
Parallel and GPU Learning: Supports parallel and GPU learning, enhancing its capability to handle complex tasks.
High-Performance: Delivers high performance, both in terms of speed and accuracy, for a variety of machine learning tasks.
Flexible and Versatile: Suitable for a range of applications, from regression to classification and ranking tasks.
Active Community and Development: Benefits from ongoing development and a growing community.
What's the difference between this Python project and similar ones?
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