gbdt
Gradient boosting decision trees.
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Download Latest Version (.zip)- feature_importance.png
- partial_dependency_plot.png
- partial_dependency_plot2.png
- ALGORITHMS.md
- INSTALL.md
- PERFORMANCE_BENCHMARK.md
- TUTORIAL.md
- example.ipynb
- README.md
- test.tsv
- train.tsv
- univariate_example.ipynb
- benchm-ml.auc.config
- benchm-ml.gbrank.config
- benchm-ml.huberized_hinge.config
- benchm-ml.logloss.config
- benchm-ml.mse.config
- benchm-ml.pairwise_logloss.config
- python_example.py
- run.py
- test.tsv
- train-0.1m.tsv
- __init__.py
- libgbdt.so
- __init__.py
- libgbdt.so
- __init__.py
- __init__.py
- libgbdt.so
- __init__.py
- __init__.py
- __init__.py
- _data_store.py
- _forest.py
- _forest_visualizer.py
- _gbdt.py
- _libgbdt.py
- _partial_dependency_plot.py
- _version.py
- bagged.py
- compute_metrics.py
- compute_rmse.py
- convert_tsv_to_filatfiles.py
- draw_trees.py
- setup.sh
- base.h
- BUILD
- bar
- foo
- foo2
- block-0-with-header.tsv
- block-0.tsv
- block-1.tsv
- block-2.tsv
- data_with_missing.tsv
- header
- BUILD
- column.cc
- column.h
- column_test.cc
- data_store.cc
- data_store.h
- data_store_test.cc
- flatfiles_data_store.cc
- flatfiles_data_store.h
- flatfiles_data_store_test.cc
- tsv_block.cc
- tsv_block.h
- tsv_block_test.cc
- tsv_data_store.cc
- tsv_data_store.h
- tsv_data_store_test.cc
- color
- height
- label
- width
- forest.model.txt
- BUILD
- compute_tree_scores.cc
- compute_tree_scores.h
- compute_tree_scores_test.cc
- evaluation.cc
- evaluation.h
- gbdt_algo.cc
- gbdt_algo.h
- gbdt_algo_test.cc
- split_algo.cc
- split_algo.h
- split_algo_test.cc
- tree_algo.cc
- tree_algo.h
- tree_algo_test.cc
- utils.cc
- utils.h
- utils_test.cc
- BUILD
- gradient_data.h
- group.cc
- group.h
- group_test.cc
- loss_func.h
- loss_func_auc.h
- loss_func_factory.cc
- loss_func_factory.h
- loss_func_factory_test.cc
- loss_func_gbrank.h
- loss_func_huberized_hinge.cc
- loss_func_huberized_hinge.h
- loss_func_huberized_hinge_test.cc
- loss_func_lambdamart.cc
- loss_func_lambdamart.h
- loss_func_lambdamart_test.cc
- loss_func_logloss.cc
- loss_func_logloss.h
- loss_func_logloss_test.cc
- loss_func_math.cc
- loss_func_math.h
- loss_func_mse.h
- loss_func_mse_test.cc
- loss_func_pairwise.cc
- loss_func_pairwise.h
- loss_func_pairwise_logloss.h
- loss_func_pairwise_test.cc
- loss_func_pointwise.cc
- loss_func_pointwise.h
- BUILD
- config.proto
- tree.proto
- BUILD
- column_py.cc
- column_py.h
- datastore_py.cc
- datastore_py.h
- forest_py.cc
- forest_py.h
- gbdt_py.cc
- gbdt_py_base.cc
- gbdt_py_base.h
- train_gbdt_py.cc
- train_gbdt_py.h
- BUILD
- json_utils.cc
- json_utils.h
- json_utils_test.cc
- stopwatch.cc
- stopwatch.h
- stopwatch_test.cc
- subsampling.cc
- subsampling.h
- subsampling_test.cc
- threadpool.cc
- threadpool.h
- utils.cc
- utils.h
- utils_inl.h
- utils_test.cc
- vector_slice.h
- vector_slice_test.cc
- BUILD
- flags.cc
- main.cc
- BUILD
- BUILD.tpl
- python_configure.bzl
- remote.BUILD.tpl
- BUILD
- cppformat.BUILD
- pybind11.BUILD
- .bazelrc
- .clang-format
- .gitignore
- LICENSE
- README.md
- setup.cfg
- setup.py
- WORKSPACE
# Installation Guide
1. Get the code
git clone https://github.com/yarny/gbdt
Downloads the entire project code from GitHub to your computer.
cd gbdt
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install .
Installs the package published on PyPI directly β no need to clone the source.
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
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