Boosting is an ensemble learning technique that combines multiple weak learners (usually decision trees) to create a strong learner. It repeatedly trains new models, focusing on instances where previous models struggled, to improve the overall performance of the model.

The principles of LightGBM revolve around efficiency, scalability, and accuracy. It achieves this by using innovative techniques such as leaf-wise tree growth, histogram-based algorithms, and efficient data handling to optimize memory usage and training time. LightGBM prioritizes speed and performance, making it suitable for handling large-scale datasets and complex models.

The superiority of LightGBM over Random Forest and XGBoost depends on the specific dataset and the task at hand. LightGBM often performs well on large-scale datasets due to its efficient algorithms and parallel processing capabilities. However, each algorithm has its own strengths and weaknesses, and the choice depends on factors such as dataset size, complexity, and computational resources.

LightGBM creates a decision tree that grows leaf-wise, meaning that with each condition, only one leaf is split based on its gain. Sometimes, especially with small datasets, leaf-wise growth can lead to overfitting. Overfitting can be prevented by limiting the depth of the tree. LightGBM uses a histogram-based approach to bin data into groups. Instead of using each data point, these groups are used to iterate, calculate gain, and split the data. This method is also beneficial for optimizing sparse datasets. Another feature of LightGBM is exclusive feature bundling, which means the algorithm combines non-overlapping features to reduce data dimensionality and increase processing speed.