What is lfgm?

LGBM, or Light Gradient Boosting Machine, is a gradient boosting framework developed by Microsoft. It's designed for efficiency and scalability, particularly with large datasets and high-dimensional feature spaces.

• Key Features: Gradient%20Boosting

• Key Benefits:

  • Faster Training Speed and Higher Efficiency
  • Lower Memory Usage
  • Better Accuracy
  • Support of Parallel, Distributed, and GPU learning
  • Capable of Handling Large-scale Data

• Core Techniques:

  • Gradient-based One-Side Sampling (GOSS): Reduces the data instances to calculate the gradient, focusing on instances with larger gradients.
  • Exclusive Feature Bundling (EFB): Bundles mutually exclusive features to reduce the number of features.
  • Leaf-wise (Best-First) Tree Growth: Grows trees leaf-wise instead of level-wise, which can lead to lower loss.

• Use Cases: Suitable for various machine learning tasks, including Classification and Regression.

• Related Concepts: Machine%20Learning, Data%20Science, Boosting.