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8. add options {input,output}_borders_file to fit function in CatBoost* in python-package
During training and prediction, CatBoost splits the range of values of each floating point feature into intervals, and uses these intervals instead of the true values. Using the same feature borders during training and prediction improves prediction accuracy. Currently python users can neither save feature borders after training, nor load them during prediction; while this is possible with the command-line CatBoost tool.
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