LightGBM is a gradient boosting framework that is designed for speed and efficiency. It is particularly well-suited for handling large datasets and high-dimensional data.
- Tree Growth: Splits the tree leaf-wise, which can lead to faster convergence compared to level-wise growth.
- Learning Rate: Similar to Gradient Descent, LightGBM uses a learning rate to control the contribution of each tree.
- DART: A variant of LightGBM known for its performance.
- Parameter Definition: Requires parameters to be defined in a dictionary for model configuration.
Key Parameters
- Learning Rate: Controls the step size at each iteration while moving toward a minimum of the loss function.
- Number of Leaves: Determines the complexity of the tree model.
Advantages
- Speed: Renowned for its speed, often outperforming other gradient boosting implementations.
- Memory Usage: Optimizes memory usage, enabling efficient handling of large datasets.
- Leaf-Wise Growth: Grows trees leaf-wise, leading to faster convergence.
- Parallel and GPU Learning: Supports parallel and GPU learning for further speedup.
Use Cases
- Large Datasets: Ideal for applications where speed is crucial.
- High-Dimensional Data: Efficient when dealing with high-dimensional data and categorical features.