зеркало из https://github.com/microsoft/LightGBM.git
[docs] refined parameters section in quick start guide doc (#1427)
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Nikita Titov
Guolin Ke
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69a3660511
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b4535b8d84
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@ -23,7 +23,7 @@ Parameters can be set both in config file and command line.
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By using command line, parameters should not have spaces before and after ``=``.
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By using config files, one line can only contain one parameter. You can use ``#`` to comment.
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If one parameter appears in both command line and config file, LightGBM will use the parameter in command line.
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If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line.
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.. start params list
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@ -42,151 +42,17 @@ And it needs an additional `query data <./Parameters.rst#io-parameters>`_ for ra
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Also, weight and query data could be specified as columns in training data in the same manner as label.
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Parameter Quick Look
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--------------------
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Parameters Quick Look
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---------------------
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The parameters format is ``key1=value1 key2=value2 ...``.
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The parameter format is ``key1=value1 key2=value2 ...``.
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Parameters can be set both in config file and command line.
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If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line.
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Some important parameters:
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- ``config``, default=\ ``""``, type=string, alias=\ ``config_file``
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- path to config file
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- ``task``, default=\ ``train``, type=enum, options=\ ``train``, ``predict``, ``convert_model``
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- ``train``, alias=\ ``training``, for training
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- ``predict``, alias=\ ``prediction``, ``test``, for prediction
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- ``convert_model``, for converting model file into if-else format, see more information in `IO Parameters <./Parameters.rst#io-parameters>`__
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- ``application``, default=\ ``regression``, type=enum,
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options=\ ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``, ``gammma``, ``tweedie``,
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``binary``, ``multiclass``, ``multiclassova``, ``xentropy``, ``xentlambda``, ``lambdarank``,
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alias=\ ``objective``, ``app``
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- regression application
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- ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``
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- ``regression_l1``, L1 loss, alias=\ ``mean_absolute_error``, ``mae``
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- ``huber``, `Huber loss`_
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- ``fair``, `Fair loss`_
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- ``poisson``, `Poisson regression`_
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- ``quantile``, `Quantile regression`_
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- ``mape``, `MAPE loss`_, alias=\ ``mean_absolute_percentage_error``
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- ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed`_
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- ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed`_
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- ``binary``, binary `log loss`_ classification application
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- multi-class classification application
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- ``multiclass``, `softmax`_ objective function, alias=\ ``softmax``
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- ``multiclassova``, `One-vs-All`_ binary objective function, alias=\ ``multiclass_ova``, ``ova``, ``ovr``
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- ``num_class`` should be set as well
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- cross-entropy application
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- ``xentropy``, objective function for cross-entropy (with optional linear weights), alias=\ ``cross_entropy``
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- ``xentlambda``, alternative parameterization of cross-entropy, alias=\ ``cross_entropy_lambda``
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- the label is anything in interval [0, 1]
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- ``lambdarank``, `lambdarank`_ application
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- the label should be ``int`` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
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- ``label_gain`` can be used to set the gain(weight) of ``int`` label
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- all values in ``label`` must be smaller than number of elements in ``label_gain``
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- ``boosting``, default=\ ``gbdt``, type=enum,
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options=\ ``gbdt``, ``rf``, ``dart``, ``goss``,
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alias=\ ``boost``, ``boosting_type``
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- ``gbdt``, traditional Gradient Boosting Decision Tree
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- ``rf``, Random Forest
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- ``dart``, `Dropouts meet Multiple Additive Regression Trees`_
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- ``goss``, Gradient-based One-Side Sampling
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- ``data``, default=\ ``""``, type=string, alias=\ ``train``, ``train_data``
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- training data, LightGBM will train from this data
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- ``valid``, default=\ ``""``, type=multi-string, alias=\ ``test``, ``valid_data``, ``test_data``
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- validation/test data, LightGBM will output metrics for these data
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- support multi validation data, separate by ``,``
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- ``num_iterations``, default=\ ``100``, type=int,
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alias=\ ``num_iteration``, ``num_tree``, ``num_trees``, ``num_round``, ``num_rounds``, ``num_boost_round``, ``n_estimators``
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- number of boosting iterations
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- ``learning_rate``, default=\ ``0.1``, type=double, alias=\ ``shrinkage_rate``
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- shrinkage rate
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- ``num_leaves``, default=\ ``31``, type=int, alias=\ ``num_leaf``
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- number of leaves in one tree
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- ``tree_learner``, default=\ ``serial``, type=enum, options=\ ``serial``, ``feature``, ``data``, ``voting``, alias=\ ``tree``
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- ``serial``, single machine tree learner
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- ``feature``, alias=\ ``feature_parallel``, feature parallel tree learner
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- ``data``, alias=\ ``data_parallel``, data parallel tree learner
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- ``voting``, alias=\ ``voting_parallel``, voting parallel tree learner
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- refer to `Parallel Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details
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- ``num_threads``, default=\ ``OpenMP_default``, type=int, alias=\ ``num_thread``, ``nthread``
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- number of threads for LightGBM
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- for the best speed, set this to the number of **real CPU cores**,
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not the number of threads (most CPU using `hyper-threading`_ to generate 2 threads per CPU core)
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- for parallel learning, should not use full CPU cores since this will cause poor performance for the network
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- ``max_depth``, default=\ ``-1``, type=int
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- limit the max depth for tree model.
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This is used to deal with over-fitting when ``#data`` is small.
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Tree still grows by leaf-wise
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- ``< 0`` means no limit
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- ``min_data_in_leaf``, default=\ ``20``, type=int, alias=\ ``min_data_per_leaf`` , ``min_data``, ``min_child_samples``
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- minimal number of data in one leaf. Can be used this to deal with over-fitting
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- ``min_sum_hessian_in_leaf``, default=\ ``1e-3``, type=double,
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alias=\ ``min_sum_hessian_per_leaf``, ``min_sum_hessian``, ``min_hessian``, ``min_child_weight``
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- minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting
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For all parameters, please refer to `Parameters <./Parameters.rst>`__.
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The most important parameters which new users should take a look to are located into `Core Parameters <./Parameters.rst#core-parameters>`__
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and the top of `Learning Control Parameters <./Parameters.rst#learning-control-parameters>`__
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sections of the full detailed list of `LightGBM's parameters <./Parameters.rst>`__.
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Run LightGBM
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------------
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@ -228,29 +94,3 @@ 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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.. _Huber loss: https://en.wikipedia.org/wiki/Huber_loss
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.. _Fair loss: https://www.kaggle.com/c/allstate-claims-severity/discussion/24520
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.. _Poisson regression: https://en.wikipedia.org/wiki/Poisson_regression
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.. _Quantile regression: https://en.wikipedia.org/wiki/Quantile_regression
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.. _MAPE loss: https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
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.. _log loss: https://en.wikipedia.org/wiki/Cross_entropy
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.. _softmax: https://en.wikipedia.org/wiki/Softmax_function
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.. _One-vs-All: https://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest
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.. _lambdarank: https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf
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.. _Dropouts meet Multiple Additive Regression Trees: https://arxiv.org/abs/1505.01866
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.. _hyper-threading: https://en.wikipedia.org/wiki/Hyper-threading
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.. _gamma-distributed: https://en.wikipedia.org/wiki/Gamma_distribution#Applications
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.. _tweedie-distributed: https://en.wikipedia.org/wiki/Tweedie_distribution#Applications
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