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LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory.
LightGBM supports parallel and GPU learning, and can handle large-scale data. It’s become widely-used for ranking, classification and many other machine learning tasks.
Features
- Histogram-based algorithms for optimal speed and memory usage
- Leaf-wise (Best-first) Tree Growth
- Optimal Split for Categorical Features
- State-of-art algorithms and parallel learning algorithms
- GPU Support
- Supports regression, binary classification, multi-classification and other applications and metrics
- DART
- L1/L2 regularization
- Bagging
- Column (feature) sub-sample
- Continued train with input GBDT model
- Continued train with the input score file
- Weighted training
- Validation metric output during training
- Multiple validation data
- Multiple metrics
- Early stopping (both training and prediction)
- Prediction for leaf index
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow LightGBM
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