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kaggle
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Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.
It can be clearly seen in chapter 6([CNN Lenet](ht
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Problem:
_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()
_catboost.pyx in _catboost.get_cat_factor_bytes_representation()
CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.
Could you also print a feature name, not o
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Summary
mypyshows some issues in LightGBM's Python package.mypy \ --exclude='python-package/compile/|python-package/build' \ --ignore-missing-imports \ python-package/18 errors in 4 files (click me)