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I'm combining a few questions together as I feel that it could benefit others.

  1. XGBoost API

    • Is there a performance boost (training time or accuracy) when you use the learning API vs the Scikit-Learn API? The Scikit_learn API will convert the pandas dataframe eventually to a DMatrix. So I'm assuming both of the to be the same.
  2. R vs Python API

    • Why are there differences in the predicted scores when you use the same hyperparameters?
    • Can set the same seed for both the packages as well, but still the predicted scores are going to be different. Is there something you need to do to ensure we get same results between them?
  3. GPU training

    • While training on the GPU, apart from setting up tree_method='gpu_hist', and giving the gpu_id of the respective GPU, Are there any other parameters of be aware of?
    • The training goes out of memory after a few iterations, how can we combat this issue?
    • Given the RAM of the GPU, can you backtrack the size of the dataframe you can train on that GPU?
    • If you have a large dataframe, are there techniques that can be used to train on the GPU without going out of memory?
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