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[docs] reduce redirects in docs links (#6181)
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@ -18,19 +18,19 @@ Data
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We used 5 datasets to conduct our comparison experiments. Details of data are listed in the following table:
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| Data | Task | Link | #Train\_Set | #Feature | Comments |
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+===========+=======================+========================================================================+=============+==========+==============================================+
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| Higgs | Binary classification | `link <https://archive.ics.uci.edu/ml/datasets/HIGGS>`__ | 10,500,000 | 28 | last 500,000 samples were used as test set |
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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+===========+=======================+=================================================================================+=============+==========+==============================================+
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| Higgs | Binary classification | `link <https://archive.ics.uci.edu/dataset/280/higgs>`__ | 10,500,000 | 28 | last 500,000 samples were used as test set |
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| Yahoo LTR | Learning to rank | `link <https://webscope.sandbox.yahoo.com/catalog.php?datatype=c>`__ | 473,134 | 700 | set1.train as train, set1.test as test |
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| MS LTR | Learning to rank | `link <https://www.microsoft.com/en-us/research/project/mslr/>`__ | 2,270,296 | 137 | {S1,S2,S3} as train set, {S5} as test set |
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| Expo | Binary classification | `link <http://stat-computing.org/dataexpo/2009/>`__ | 11,000,000 | 700 | last 1,000,000 samples were used as test set |
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| Expo | Binary classification | `link <https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009>`__ | 11,000,000 | 700 | last 1,000,000 samples were used as test set |
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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| Allstate | Binary classification | `link <https://www.kaggle.com/c/ClaimPredictionChallenge>`__ | 13,184,290 | 4228 | last 1,000,000 samples were used as test set |
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+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
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Environment
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^^^^^^^^^^^
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@ -291,7 +291,7 @@ References
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.. _On Grouping for Maximum Homogeneity: https://www.tandfonline.com/doi/abs/10.1080/01621459.1958.10501479
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.. _Optimization of collective communication operations in MPICH: https://www.mcs.anl.gov/~thakur/papers/ijhpca-coll.pdf
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.. _Optimization of collective communication operations in MPICH: https://web.cels.anl.gov/~thakur/papers/ijhpca-coll.pdf
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.. _A Communication-Efficient Parallel Algorithm for Decision Tree: http://papers.nips.cc/paper/6381-a-communication-efficient-parallel-algorithm-for-decision-tree
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@ -194,7 +194,7 @@ following article:
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Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018.
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.. _link1: https://archive.ics.uci.edu/ml/datasets/HIGGS
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.. _link1: https://archive.ics.uci.edu/dataset/280/higgs
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.. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
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@ -202,9 +202,9 @@ Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boos
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.. _link4: https://webscope.sandbox.yahoo.com/catalog.php?datatype=c
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.. _link5: http://research.microsoft.com/en-us/projects/mslr/
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.. _link5: https://www.microsoft.com/en-us/research/project/mslr/
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.. _link6: http://stat-computing.org/dataexpo/2009/
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.. _link6: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009
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.. _0bb4a82: https://github.com/microsoft/LightGBM/commit/0bb4a82
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@ -950,7 +950,7 @@ gcc
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.. _RDMA: https://en.wikipedia.org/wiki/Remote_direct_memory_access
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.. _MS MPI: https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes
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.. _MS MPI: https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes
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.. _Open MPI: https://www.open-mpi.org/
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@ -518,7 +518,7 @@ See `the mars documentation`_ for usage examples.
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.. _the Dask DataFrame documentation: https://docs.dask.org/en/latest/dataframe.html
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.. _the Dask prediction example: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask/prediction.py
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.. _the Dask prediction example: https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/dask/prediction.py
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.. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html
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@ -536,7 +536,7 @@ See `the mars documentation`_ for usage examples.
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.. _lightgbm_ray: https://github.com/ray-project/lightgbm_ray
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.. _Ray: https://ray.io/
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.. _Ray: https://www.ray.io/
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.. _the lightgbm_ray documentation: https://docs.ray.io/en/latest/tune/api_docs/integration.html#lightgbm-tune-integration-lightgbm
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@ -119,7 +119,7 @@ Core Parameters
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- ranking application
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- ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
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- ``lambdarank``, `lambdarank <https://proceedings.neurips.cc/paper_files/paper/2006/file/af44c4c56f385c43f2529f9b1b018f6a-Paper.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
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- ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
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@ -536,15 +536,15 @@ Learning Control Parameters
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- ``basic``, the most basic monotone constraints method. It does not slow the library at all, but over-constrains the predictions
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- ``intermediate``, a `more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
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- ``intermediate``, a `more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
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- ``advanced``, an `even more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
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- ``advanced``, an `even more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
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- ``monotone_penalty`` :raw-html:`<a id="monotone_penalty" title="Permalink to this parameter" href="#monotone_penalty">🔗︎</a>`, default = ``0.0``, type = double, aliases: ``monotone_splits_penalty``, ``ms_penalty``, ``mc_penalty``, constraints: ``monotone_penalty >= 0.0``
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- used only if ``monotone_constraints`` is set
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- `monotone penalty <https://hal.archives-ouvertes.fr/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
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- `monotone penalty <https://hal.science/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
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- if ``0.0`` (the default), no penalization is applied
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@ -564,7 +564,7 @@ Learning Control Parameters
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- **Note**: the forced split logic will be ignored, if the split makes gain worse
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- see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
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- see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/binary_classification/forced_splits.json>`__ as an example
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- ``refit_decay_rate`` :raw-html:`<a id="refit_decay_rate" title="Permalink to this parameter" href="#refit_decay_rate">🔗︎</a>`, default = ``0.9``, type = double, constraints: ``0.0 <= refit_decay_rate <= 1.0``
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@ -770,7 +770,7 @@ Dataset Parameters
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- ``enable_bundle`` :raw-html:`<a id="enable_bundle" title="Permalink to this parameter" href="#enable_bundle">🔗︎</a>`, default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle``
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- set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`__
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- set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html>`__
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- **Note**: disabling this may cause the slow training speed for sparse datasets
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@ -894,7 +894,7 @@ Dataset Parameters
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- ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)
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- see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example
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- see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/regression/forced_bins.json>`__ as an example
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- ``save_binary`` :raw-html:`<a id="save_binary" title="Permalink to this parameter" href="#save_binary">🔗︎</a>`, default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file``
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@ -961,7 +961,7 @@ Predict Parameters
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- produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
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- **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
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- **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/shap>`__
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- **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
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@ -85,4 +85,4 @@ Examples
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.. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/
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.. _Expo data: http://stat-computing.org/dataexpo/2009/
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.. _Expo data: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009
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@ -150,7 +150,7 @@ struct Config {
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// descl2 = ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda``
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// descl2 = label is anything in interval [0, 1]
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// desc = ranking application
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// descl2 = ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
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// descl2 = ``lambdarank``, `lambdarank <https://proceedings.neurips.cc/paper_files/paper/2006/file/af44c4c56f385c43f2529f9b1b018f6a-Paper.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
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// descl2 = ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
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// descl2 = ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank``
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// descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
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@ -501,14 +501,14 @@ struct Config {
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// desc = used only if ``monotone_constraints`` is set
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// desc = monotone constraints method
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// descl2 = ``basic``, the most basic monotone constraints method. It does not slow the library at all, but over-constrains the predictions
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// descl2 = ``intermediate``, a `more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
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// descl2 = ``advanced``, an `even more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
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// descl2 = ``intermediate``, a `more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
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// descl2 = ``advanced``, an `even more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
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std::string monotone_constraints_method = "basic";
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// alias = monotone_splits_penalty, ms_penalty, mc_penalty
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// check = >=0.0
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// desc = used only if ``monotone_constraints`` is set
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// desc = `monotone penalty <https://hal.archives-ouvertes.fr/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
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// desc = `monotone penalty <https://hal.science/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
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// desc = if ``0.0`` (the default), no penalization is applied
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double monotone_penalty = 0.0;
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@ -524,7 +524,7 @@ struct Config {
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// desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits
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// desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values
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// desc = **Note**: the forced split logic will be ignored, if the split makes gain worse
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// desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
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// desc = see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/binary_classification/forced_splits.json>`__ as an example
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std::string forcedsplits_filename = "";
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// check = >=0.0
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@ -683,7 +683,7 @@ struct Config {
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bool is_enable_sparse = true;
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// alias = is_enable_bundle, bundle
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// desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`__
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// desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html>`__
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// desc = **Note**: disabling this may cause the slow training speed for sparse datasets
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bool enable_bundle = true;
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@ -770,7 +770,7 @@ struct Config {
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// desc = path to a ``.json`` file that specifies bin upper bounds for some or all features
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// desc = ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)
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// desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example
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// desc = see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/regression/forced_bins.json>`__ as an example
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std::string forcedbins_filename = "";
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// [no-save]
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@ -826,7 +826,7 @@ struct Config {
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// desc = used only in ``prediction`` task
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// desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributes to each prediction
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// desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
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// desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
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// desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/shap>`__
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// desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
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// desc = **Note**: this feature is not implemented for linear trees
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bool predict_contrib = false;
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