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LightGBM Regressor

The LightGBM Regressor is a gradient boosting framework based on decision trees from the LightGBM library. It uses a novel technique called Gradient-based One-Side Sampling (GOSS) to filter out the data instances for finding a split value, and Exclusive Feature Bundling (EFB) to reduce the number of features during training. This results in a faster training process and lower memory usage while maintaining high accuracy.

Advantages:​

  • Faster training speed and lower memory usage compared to other gradient boosting frameworks.
  • High accuracy and good generalization performance.
  • Supports parallel and GPU learning.
  • Handles large-scale data and high-dimensional features well.

Disadvantages:​

  • Requires careful tuning of hyperparameters for optimal performance.
  • Less interpretable than simpler models like linear regression.
  • Not as robust to noise and outliers as some other models.