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The variation in performance gains you are seeing from reduction precision might be do to different frameworks using different types. Even after downcasting data types, some operations will automatically upcast types. You mention using Pandas and TensorFlow / Keras. Mixing these frameworks leads to unwanted recasting of data types. It is better to use a ...


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Try to give sklearn wrapper of xgboost to sklearn OneVsRestClassifier. https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn If it doesn't work you can create four different label arrays in which samples belong to corresponding class labeled as 1s, and others as 0. Then training with each of the label arrays you can get ...


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Every Machine Learning Problem is different, so there is no standard answer to your question. For the problem you're working on maybe a 70-30 train-test split would result in an optimal model which performs equally well on the test dataset, whereas for another problem may be that ratio just won't do any justice to the model. It's all about experimentation. ...


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