Light GBM
LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be efficient and scalable, and is particularly well suited for large datasets or datasets with many features. LightGBM is an open-source project developed by Microsoft, it is based on the popular GBM algorithm, but with several enhancements that make it faster and more accurate.
The main features of LightGBM include:
Gradient-based One-Side Sampling (GOSS): This technique is used to reduce the number of data points used in the tree-building process, which speeds up training times.
Exclusive Feature Bundling (EFB): This technique is used to bundle similar features together, which reduces the number of features and speeds up training times.
Leaf-wise Tree Growth: This technique is used to grow the tree by adding new leaves to the leaf with the highest gradient, which leads to deeper and more accurate trees.
LightGBM also includes other techniques such as histogram-based algorithms for faster training and the ability to handle categorical features.
The pros of LightGBM are:
It is fast and efficient, particularly for large datasets or datasets with many features.
It supports parallel and GPU learning, which can further speed up training times.
It has a variety of techniques for handling categorical features and missing values, making it more flexible than other tree-based algorithms.
It is suitable for both classification and regression tasks
The cons of LightGBM are:
It requires a large amount of memory to store the tree structure and the data.
It can be affected by irrelevant or redundant features in the data.
It is sensitive to the initial parameter settings and requires tuning to achieve optimal performance.
LightGBM is widely used in many real-world applications such as recommendation systems, click prediction, anomaly detection, and more.
The main features of LightGBM include:
Gradient-based One-Side Sampling (GOSS): This technique is used to reduce the number of data points used in the tree-building process, which speeds up training times.
Exclusive Feature Bundling (EFB): This technique is used to bundle similar features together, which reduces the number of features and speeds up training times.
Leaf-wise Tree Growth: This technique is used to grow the tree by adding new leaves to the leaf with the highest gradient, which leads to deeper and more accurate trees.
LightGBM also includes other techniques such as histogram-based algorithms for faster training and the ability to handle categorical features.
The pros of LightGBM are:
It is fast and efficient, particularly for large datasets or datasets with many features.
It supports parallel and GPU learning, which can further speed up training times.
It has a variety of techniques for handling categorical features and missing values, making it more flexible than other tree-based algorithms.
It is suitable for both classification and regression tasks
The cons of LightGBM are:
It requires a large amount of memory to store the tree structure and the data.
It can be affected by irrelevant or redundant features in the data.
It is sensitive to the initial parameter settings and requires tuning to achieve optimal performance.
LightGBM is widely used in many real-world applications such as recommendation systems, click prediction, anomaly detection, and more.
Updated on: 30/01/2023
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