What is Light GBM?
LightGBM is a fast and accurate open-source gradient boosting framework. It has been developed by Microsoft Research, it is written in C++ and provides interfaces for Python, R and Scala, and it is currently being used by many large companies to train their machine learning models.
LightGBM trains models with a parallelized tree architecture, which is designed to scale up to thousands of CPU cores or across hundreds of GPUs in a single job. The training process is very efficient in terms of memory usage and speed, and the model can be used for prediction with high accuracy on large datasets.
LightGBM can be used for predictive modeling, ranking, classification, regression, and more. It can be used with any type of data whether it be categorical or continuous. LightGBM has many features that make it a great machine learning algorithm such as its ability to handle large datasets and its high efficiency in training models.
Architecture: The capacity of level-wise algorithms to grow the tree in more efficient ways has been noted before. However, this does not mean that leaf-wise boosting techniques are waiting for extinction. In fact, there are many advantages with leaf-level boosting algorithms – namely the lower chance of overfitting, as every node will have data from all trees. Smaller datasets might be prone to overfitting which is why scientists suggest modeling Leaf-Wise Tree Growth to keep it simple.
