xgboost

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.

Is your feature request related to a problem? Please describe.
Implements classification_report for classification metrics.(https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)

Our xgboost models use the binary:logistic' objective function, however the m2cgen converted version of the models return raw scores instead of the transformed scores.

This is fine as long as the user knows this is happening! I didn't, so it took a while to figure out what was going on. I'm wondering if perhaps a useful warning could be raised for users to alert them of this issue? A warning

I trained a dataset with sample weight using 3 algos: LightGBM, Xgboost, and CatBoost.
I found that the learning curve chart for CatBoost doesn't take into account the sample weight but the score in the table does.
Maybe you forgot to put sample_weight for CatBoost charts? I also see the problems in the ROC curve chart (but it's the same behavior among all models).
Also, could this affect the t

/kind feature

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Add a sample of MMS using a custom model server similar to sklearn and triton that will reside [in

See https://github.com/tensorflow/transform/blob/master/tensorflow_transform/mappers.py#L374