To Improve Model fastness we can use LightGBM and CatBoost

[praveenarjun]

LightGBM:
Efficiency: LightGBM is designed to be highly efficient and can handle large datasets with faster training times.
Accuracy: It often provides better accuracy compared to other gradient boosting algorithms.
Scalability: LightGBM can handle large-scale data and high-dimensional features.
Support for Categorical Features: It can directly handle categorical features without the need for one-hot encoding.

CatBoost:
Handling Categorical Features: CatBoost is specifically designed to handle categorical features effectively, reducing the need for extensive preprocessing.
Robustness: It is less prone to overfitting and provides robust performance on various datasets.
Ease of Use: CatBoost requires minimal parameter tuning and is easy to use.
Efficiency: It is optimized for fast training and prediction times.

These are the models that contains data structure like tree so the time complexity may reduce like logn

[praveenarjun]

Please assign this issue to me and add a relevant label. Thank you!

[rohitinu6]

@praveenarjun all the best, will add the labels

[praveenarjun]

I have Done the changes If any Query You can commit and pls give me Labels and level

[github-actions]

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