2eee87423c
We have 2 new custom objective demos covering both regression and classification with accompanying tutorials in documents.
19 lines
1023 B
Markdown
19 lines
1023 B
Markdown
XGBoost Python Feature Walkthrough
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* [Basic walkthrough of wrappers](basic_walkthrough.py)
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* [Re-implement RMSLE as customized metric and objective](custom_rmsle.py)
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* [Re-Implement `multi:softmax` objective as customized objective](custom_softmax.py)
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* [Boosting from existing prediction](boost_from_prediction.py)
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* [Predicting using first n trees](predict_first_ntree.py)
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* [Generalized Linear Model](generalized_linear_model.py)
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* [Cross validation](cross_validation.py)
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* [Predicting leaf indices](predict_leaf_indices.py)
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* [Sklearn Wrapper](sklearn_examples.py)
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* [Sklearn Parallel](sklearn_parallel.py)
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* [Sklearn access evals result](sklearn_evals_result.py)
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* [Access evals result](evals_result.py)
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* [External Memory](external_memory.py)
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* [Training continuation](continuation.py)
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* [Feature weights for column sampling](feature_weights.py)
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* [Basic Categorical data support](categorical.py)
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* [Compare builtin categorical data support with one-hot encoding](cat_in_the_dat.py) |