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Currently I am working on a project which uses Xgboost Regression.

Before putting data into model, I implemented Normalization, the accuracy significantly increased compared with without Normalization.

But I saw someone said that Xgboost doesn’t need Normalization .

Did someone meet the same issue as I met ?

Thanks in advance.

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Xgboost is an ensemble algorithm based on decision trees, so doesn't need normalization. You can check this on Xgboost official github: Is Normalization necessary? and this post What are the implications of scaling the features to xgboost?

I'm new in this algorithm but I'm pretty sure of what I've written

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