CatBoost is an open-source gradient boosting library used to train large amounts of data using ML. It supports the direct usage of categorical variables. It gives a very high performance in comparison to the other boosting algorithms. It is straightforward to implement and run. It is a model developed by Yandex. It provides support for out-of-the-box descriptive data formats and does not require much training. It gives a good performance with a lesser number of training iterations.
Light GBM
LightGBM is a gradient boosting framework that uses a decision tree algorithm. As the name suggests, its training speed is very fast and can be used for training large datasets.
Pros
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Faster training speed and accuracy
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Lower memory usage
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Parallel GPU support
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Higher efficiency and performance
Cons
- Narrow user base