[PYTHON] Erklärung aller lightGBM-Parameter (unterwegs)

Inhalt

Ich werde alle Parameter von lightGBM grob erklären. Da es viele Inhalte gibt, werde ich sie über mehrere Tage langsam übersetzen. Ich werde die Details von Zeit zu Zeit in einem separaten Artikel aktualisieren. Wenn Sie einen Fehler machen, würde ich es begrüßen, wenn Sie darauf hinweisen könnten. Der offizielle Github von lightGBM ist hier

Das grundlegende Beschreibungsformat ist Standard = Standard, Typ = Typ, Optionen = Optionen, Einschränkungen = Einschränkungen

Kernparameter

-- config, default = "", type = string, alias: config_file

-- task, default = train, type = enum, options: train, prognost```, convert_model, refit, alias: task_type ``

-- train, alias: training

-- convert_model, Konvertiert die Modelldatei in das if-else-Format. Weitere Informationen finden Sie unter E / A-Parameter.

-- refit, refit mit neuen Daten, Alias: refit_tree

--Rückkehr

-- Regression, L2-Verlust, Aliase: Regression_l2, l2, mean_squared_error, mse, l2_root, root_mean_squared_error, rmse

-- Gamma, Gamma-Regression mit Log-Link. Anwendungsbeispiel: Fälle, in denen die Häufigkeit des Versicherungsschutzes modelliert wird, und andere Fälle, in denen die Gammaverteilung verfolgt wird. Gamma-verteilt

-- tweedie, Tweedie-Regression mit Log-Link. Anwendungsbeispiel: Modellierung des Totalverlusts der Versicherung und anderer Fälle nach Tweedie-Verteilung [tweedie-verteilt](https://en.wikipedia.org/wiki/ Tweedie_distribution # Occurrence_and_applications)

--Dichotomie

-- binär, binär Protokollverlust (oder logistische Regression)

--Label muss 0 oder 1 sein; [0,1] Allgemeine Wahrscheinlichkeiten von Labels finden Sie unter Cross Entropy (https://en.wikipedia.org/wiki/Cross_entropy)

-- multiclass, Softmax, Alias: softmax

-- multiclassova, One-vs-All, Alias: multiclass_ova, ova , ovr

  -  ``num_class`` should be set as well

-- cross_entropy, objektive Funktion der Kreuzentropie (Gewicht ist willkürlich), Alias: xentropy

-- cross_entropy_lambda, andere Parametrisierung der Kreuzentropie, Alias: xentlambda

  -  label is anything in interval [0, 1]

-- lambdarank, lambdarank. label_gain (Definitionsbuch) (Erläuterung ab Seite) hat ganzzahlige Beschriftungen und gewichtet jeden Wert der Beschriftung so, dass er kleiner als die Anzahl der Elemente in label_gain ist.

-- rank_xendcg, XE_NDCG_MART Rangzielfunktion, Alias: xendcg, xe_ndcg, xe_ndcg_mart, xendcg_mart

-- rank_xendcg Die Berechnung ist schnell und das Verhalten ist ähnlich wie bei Lambdarank.

-- boosting, default = gbdt, type = enum, options: gbdt, rf, dart, goss, alias: boosting_type, boost

-- gbdt, typische Gradientenverstärkung, auch bekannt als: gbrt

-- rf, zufälliger Baum, Alias: random_forest

-- data, default = "", type = string, Aliase: train, train_data, train_data_file, data_filename

-- valid, default = "", type = string, Aliase: test, valid_data, valid_data_file, test_data, test_data_file` `,` `valid_filenames

-- num_iterations, default = 100, type = int, Aliase: num_iteration, n_iter, num_tree, num_trees, num_round , num_rounds, num_boost_round, n_estimators, Constraints: num_iterations> = 0

-- learning_rate, default = 0.1, type = double, alias: shrinkage_rate, eta, Einschränkung: learning_rate> 0.0

-- num_leaves, default = 31, type = int, Aliase: num_leaf, max_leaves, max_leaf, Einschränkungen: 1 <num_leaves <= 131072`

-- tree_learner, default = serial, type = enum, Optionen: serial, feature, data, abstimmen, Alias: tree, tree_type, tree_learner_type

-- feature, Feature Parallel Tree Learner, Alias: feature_parallel

-- data, Datenparalleler Baumlerner, Alias: data_parallel

-- Voting, Voting Parallel Tree Learner, Alias: Voting_parallel

--Task Manager und andere CPU-Überwachungstools zeigen möglicherweise an, dass nicht alle Kerne verwendet werden. ** Das ist normal **

Kontrollparameter lernen

-- num_threads ist groß, zB > 20

--Wenn Sie beschleunigen möchten, indem Sie einen kleinen Wert "bagging_fraction" oder "goss" verwenden

-- <0 bedeutet unbegrenzt

-- <= 0 bedeutet unbegrenzt.

-- min_data_in_leaf, default = 20, type = int, Aliase: min_data_per_leaf, min_data, min_child_samples, Einschränkungen: min_data_in_leaf> = 0

-- bagging_fraction, default = 1.0, type = double, alias: sub_row, subsample, bagging, Einschränkung: 0.0 <bagging_fraction <= 1.0`

-- pos_bagging_fraction, default = 1.0, type = double, Aliase: pos_sub_row, pos_subsample, pos_bagging, Einschränkungen: 0.0 <pos_bagging_fraction <= 1.0`

-- neg_bagging_fraction, default = 1.0, type = double, Aliase: neg_sub_row, neg_subsample, neg_bagging, Einschränkungen: 0.0 <neg_bagging_fraction <= 1.0`

--Verwenden Sie mit pos_bagging_fraction.

-- bagging_freq, default = 0, type = int, alias: subsample_freq

-- 0 bedeutet kein Absacken. ; k bedeutet, dass es wiederholt einmal pro k verpackt wird.

-- bagging_seed, default = 3, type = int, alias: bagging_fraction_seed

--Bagging zufälliger Samen

-- feature_fraction, default = 1.0, type = double, alias: sub_feature, colsample_bytree, Einschränkung: 0.0 <feature_fraction <= 1.0

--Wenn feature_fraction kleiner als 1.0 ist, extrahiert LightGBM jedes Mal zufällig ein Teilmerkmal. Beispielsweise wählt LightGBM mit 0.8 vor dem Training 80% der Funktionen aus.

-- feature_fraction_bynode, default = 1.0, type = double, alias: sub_feature_bynode, colsample_bynode, Einschränkung: 0.0 <feature_fraction_bynode <= 1.0

--Wenn feature_fraction_bynode kleiner als 1.0 ist, extrahiert LightGBM die Features an jedem Baumknoten teilweise. Beispielsweise extrahiert LightGBM mit "0,8" 80% der Features aus jedem Baumknoten.

--Wenn true, wählt lightGBM bei der Auswertung von Knotensplits nur einen zufälligen Schwellenwert für jedes Feature aus.

-- Early_stopping_round, Standard = 0, Typ = int, Aliase: Early_stopping_rounds, Early_stopping, n_iter_no_change

-- <= 0 bedeutet ungültig.

-- max_delta_step, default = 0.0, type = double, Aliase: max_tree_output, max_leaf_output

-- <= 0 bedeutet unbegrenzt.

-- lambda_l1, default = 0.0, type = double, alias: reg_alpha, limit: lambda_l1> = 0.0

--L1 Regularisierung

-- lambda_l2, default = 0.0, type = double, alias: reg_lambda, lambda, limit: lambda_l2> = 0.0

--L2-Regularisierung

-- min_gain_to_split, default = 0.0, type = double, alias: min_split_gain, limit: min_gain_to_split> = 0.0

-- drop_rate, default = 0.1, type = double, alias: rate_drop, Einschränkung: 0.0 <= drop_rate <= 1.0

--dropout rate: Aussetzer werden verwendet, um den zufälligen Teil der Funktion während des Trainings zu schwächen. um einen zufälligen Bruchteil der Eingabefunktionen während der Trainingsphase stummzuschalten. Referenzen)

IO Parameters

Dataset Parameters


-  ``max_bin`` , default = ``255``, type = int, constraints: ``max_bin > 1``

   -  max number of bins that feature values will be bucketed in

   -  small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)

   -  LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``

-  ``max_bin_by_feature`` , default = ``None``, type = multi-int

   -  max number of bins for each feature

   -  if not specified, will use ``max_bin`` for all features

-  ``min_data_in_bin`` , default = ``3``, type = int, constraints: ``min_data_in_bin > 0``

   -  minimal number of data inside one bin

   -  use this to avoid one-data-one-bin (potential over-fitting)

-  ``bin_construct_sample_cnt`` , default = ``200000``, type = int, aliases: ``subsample_for_bin``, constraints: ``bin_construct_sample_cnt > 0``

   -  number of data that sampled to construct histogram bins

   -  setting this to larger value will give better training result, but will increase data loading time

   -  set this to larger value if data is very sparse

-  ``data_random_seed`` , default = ``1``, type = int, aliases: ``data_seed``

   -  random seed for sampling data to construct histogram bins

-  ``is_enable_sparse`` , default = ``true``, type = bool, aliases: ``is_sparse``, ``enable_sparse``, ``sparse``

   -  used to enable/disable sparse optimization

-  ``enable_bundle`` , default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle``

   -  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>`__

   -  **Note**: disabling this may cause the slow training speed for sparse datasets

-  ``use_missing`` , default = ``true``, type = bool

   -  set this to ``false`` to disable the special handle of missing value

-  ``zero_as_missing`` , default = ``false``, type = bool

   -  set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices)

   -  set this to ``false`` to use ``na`` for representing missing values

-  ``feature_pre_filter`` , default = ``true``, type = bool

   -  set this to ``true`` to pre-filter the unsplittable features by ``min_data_in_leaf``

   -  as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object

   -  **Note**: setting this to ``false`` may slow down the training

-  ``pre_partition`` , default = ``false``, type = bool, aliases: ``is_pre_partition``

   -  used for parallel learning (excluding the ``feature_parallel`` mode)

   -  ``true`` if training data are pre-partitioned, and different machines use different partitions

-  ``two_round`` , default = ``false``, type = bool, aliases: ``two_round_loading``, ``use_two_round_loading``

   -  set this to ``true`` if data file is too big to fit in memory

   -  by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big

   -  **Note**: works only in case of loading data directly from file

-  ``header`` , default = ``false``, type = bool, aliases: ``has_header``

   -  set this to ``true`` if input data has header

   -  **Note**: works only in case of loading data directly from file

-  ``label_column`` , default = ``""``, type = int or string, aliases: ``label``

   -  used to specify the label column

   -  use number for index, e.g. ``label=0`` means column\_0 is the label

   -  add a prefix ``name:`` for column name, e.g. ``label=name:is_click``

   -  **Note**: works only in case of loading data directly from file

-  ``weight_column`` , default = ``""``, type = int or string, aliases: ``weight``

   -  used to specify the weight column

   -  use number for index, e.g. ``weight=0`` means column\_0 is the weight

   -  add a prefix ``name:`` for column name, e.g. ``weight=name:weight``

   -  **Note**: works only in case of loading data directly from file

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0, and weight is column\_1, the correct parameter is ``weight=0``

-  ``group_column`` , default = ``""``, type = int or string, aliases: ``group``, ``group_id``, ``query_column``, ``query``, ``query_id``

   -  used to specify the query/group id column

   -  use number for index, e.g. ``query=0`` means column\_0 is the query id

   -  add a prefix ``name:`` for column name, e.g. ``query=name:query_id``

   -  **Note**: works only in case of loading data directly from file

   -  **Note**: data should be grouped by query\_id

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0 and query\_id is column\_1, the correct parameter is ``query=0``

-  ``ignore_column`` , default = ``""``, type = multi-int or string, aliases: ``ignore_feature``, ``blacklist``

   -  used to specify some ignoring columns in training

   -  use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored

   -  add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored

   -  **Note**: works only in case of loading data directly from file

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``

   -  **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully

-  ``categorical_feature`` , default = ``""``, type = multi-int or string, aliases: ``cat_feature``, ``categorical_column``, ``cat_column``

   -  used to specify categorical features

   -  use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features

   -  add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features

   -  **Note**: only supports categorical with ``int`` type (not applicable for data represented as pandas DataFrame in Python-package)

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``

   -  **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)

   -  **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero

   -  **Note**: all negative values will be treated as **missing values**

   -  **Note**: the output cannot be monotonically constrained with respect to a categorical feature

-  ``forcedbins_filename`` , default = ``""``, type = string

   -  path to a ``.json`` file that specifies bin upper bounds for some or all features

   -  ``.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)

   -  see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example

-  ``save_binary`` , default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file``

   -  if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time

   -  **Note**: ``init_score`` is not saved in binary file

   -  **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function

Predict Parameters

Convert Parameters


-  ``convert_model_language`` , default = ``""``, type = string

   -  used only in ``convert_model`` task

   -  only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen <https://github.com/BayesWitnesses/m2cgen>`__ utility

   -  if ``convert_model_language`` is set and ``task=train``, the model will be also converted

   -  **Note**: can be used only in CLI version

-  ``convert_model`` , default = ``gbdt_prediction.cpp``, type = string, aliases: ``convert_model_file``

   -  used only in ``convert_model`` task

   -  output filename of converted model

   -  **Note**: can be used only in CLI version

Objective Parameters
--------------------

-  ``objective_seed`` , default = ``5``, type = int

   -  used only in ``rank_xendcg`` objective

   -  random seed for objectives, if random process is needed

-  ``num_class`` , default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0``

   -  used only in ``multi-class`` classification application

-  ``is_unbalance`` , default = ``false``, type = bool, aliases: ``unbalance``, ``unbalanced_sets``

   -  used only in ``binary`` and ``multiclassova`` applications

   -  set this to ``true`` if training data are unbalanced

   -  **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities

   -  **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them

-  ``scale_pos_weight`` , default = ``1.0``, type = double, constraints: ``scale_pos_weight > 0.0``

   -  used only in ``binary`` and ``multiclassova`` applications

   -  weight of labels with positive class

   -  **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities

   -  **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them

-  ``sigmoid`` , default = ``1.0``, type = double, constraints: ``sigmoid > 0.0``

   -  used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications

   -  parameter for the sigmoid function

-  ``boost_from_average`` , default = ``true``, type = bool

   -  used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications

   -  adjusts initial score to the mean of labels for faster convergence

-  ``reg_sqrt`` , default = ``false``, type = bool

   -  used only in ``regression`` application

   -  used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2``

   -  might be useful in case of large-range labels

-  ``alpha`` , default = ``0.9``, type = double, constraints: ``alpha > 0.0``

   -  used only in ``huber`` and ``quantile`` ``regression`` applications

   -  parameter for `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__ and `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__

-  ``fair_c`` , default = ``1.0``, type = double, constraints: ``fair_c > 0.0``

   -  used only in ``fair`` ``regression`` application

   -  parameter for `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__

-  ``poisson_max_delta_step`` , default = ``0.7``, type = double, constraints: ``poisson_max_delta_step > 0.0``

   -  used only in ``poisson`` ``regression`` application

   -  parameter for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__ to safeguard optimization

-  ``tweedie_variance_power`` , default = ``1.5``, type = double, constraints: ``1.0 <= tweedie_variance_power < 2.0``

   -  used only in ``tweedie`` ``regression`` application

   -  used to control the variance of the tweedie distribution

   -  set this closer to ``2`` to shift towards a **Gamma** distribution

   -  set this closer to ``1`` to shift towards a **Poisson** distribution

-  ``lambdarank_truncation_level`` , default = ``20``, type = int, constraints: ``lambdarank_truncation_level > 0``

   -  used only in ``lambdarank`` application

   -  used for truncating the max DCG, refer to "truncation level" in the Sec. 3 of `LambdaMART paper <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf>`__

-  ``lambdarank_norm`` , default = ``true``, type = bool

   -  used only in ``lambdarank`` application

   -  set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data

   -  set this to ``false`` to enforce the original lambdarank algorithm

-  ``label_gain`` , default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double

   -  used only in ``lambdarank`` application

   -  relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains

   -  separate by ``,``

Metric Parameters
-----------------

-  ``metric`` , default = ``""``, type = multi-enum, aliases: ``metrics``, ``metric_types``

   -  metric(s) to be evaluated on the evaluation set(s)

      -  ``""`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added)

      -  ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom``

      -  ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1``

      -  ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``

      -  ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root``

      -  ``quantile``, `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__

      -  ``mape``, `MAPE loss <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`__, aliases: ``mean_absolute_percentage_error``

      -  ``huber``, `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__

      -  ``fair``, `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__

      -  ``poisson``, negative log-likelihood for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__

      -  ``gamma``, negative log-likelihood for **Gamma** regression

      -  ``gamma_deviance``, residual deviance for **Gamma** regression

      -  ``tweedie``, negative log-likelihood for **Tweedie** regression

      -  ``ndcg``, `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``

      -  ``map``, `MAP <https://makarandtapaswi.wordpress.com/2012/07/02/intuition-behind-average-precision-and-map/>`__, aliases: ``mean_average_precision``

      -  ``auc``, `AUC <https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve>`__

      -  ``binary_logloss``, `log loss <https://en.wikipedia.org/wiki/Cross_entropy>`__, aliases: ``binary``

      -  ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification

      -  ``auc_mu``, `AUC-mu <http://proceedings.mlr.press/v97/kleiman19a/kleiman19a.pdf>`__

      -  ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``

      -  ``multi_error``, error rate for multi-class classification

      -  ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy``

      -  ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda``

      -  ``kullback_leibler``, `Kullback-Leibler divergence <https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence>`__, aliases: ``kldiv``

   -  support multiple metrics, separated by ``,``

-  ``metric_freq`` , default = ``1``, type = int, aliases: ``output_freq``, constraints: ``metric_freq > 0``

   -  frequency for metric output

   -  **Note**: can be used only in CLI version

-  ``is_provide_training_metric`` , default = ``false``, type = bool, aliases: ``training_metric``, ``is_training_metric``, ``train_metric``

   -  set this to ``true`` to output metric result over training dataset

   -  **Note**: can be used only in CLI version

-  ``eval_at`` , default = ``1,2,3,4,5``, type = multi-int, aliases: ``ndcg_eval_at``, ``ndcg_at``, ``map_eval_at``, ``map_at``

   -  used only with ``ndcg`` and ``map`` metrics

   -  `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__ and `MAP <https://makarandtapaswi.wordpress.com/2012/07/02/intuition-behind-average-precision-and-map/>`__ evaluation positions, separated by ``,``

-  ``multi_error_top_k`` , default = ``1``, type = int, constraints: ``multi_error_top_k > 0``

   -  used only with ``multi_error`` metric

   -  threshold for top-k multi-error metric

   -  the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise

      -  more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class

   -  when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric

-  ``auc_mu_weights`` , default = ``None``, type = multi-double

   -  used only with ``auc_mu`` metric

   -  list representing flattened matrix (in row-major order) giving loss weights for classification errors

   -  list should have ``n * n`` elements, where ``n`` is the number of classes

   -  the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list

   -  if not specified, will use equal weights for all classes

Network Parameters
------------------

-  ``num_machines`` , default = ``1``, type = int, aliases: ``num_machine``, constraints: ``num_machines > 0``

   -  the number of machines for parallel learning application

   -  this parameter is needed to be set in both **socket** and **mpi** versions

-  ``local_listen_port`` , default = ``12400``, type = int, aliases: ``local_port``, ``port``, constraints: ``local_listen_port > 0``

   -  TCP listen port for local machines

   -  **Note**: don't forget to allow this port in firewall settings before training

-  ``time_out`` , default = ``120``, type = int, constraints: ``time_out > 0``

   -  socket time-out in minutes

-  ``machine_list_filename`` , default = ``""``, type = string, aliases: ``machine_list_file``, ``machine_list``, ``mlist``

   -  path of file that lists machines for this parallel learning application

   -  each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)

-  ``machines`` , default = ``""``, type = string, aliases: ``workers``, ``nodes``

   -  list of machines in the following format: ``ip1:port1,ip2:port2``

GPU Parameters
--------------

-  ``gpu_platform_id`` , default = ``-1``, type = int

   -  OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform

   -  ``-1`` means the system-wide default platform

   -  **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details

-  ``gpu_device_id`` , default = ``-1``, type = int

   -  OpenCL device ID in the specified platform. Each GPU in the selected platform has a unique device ID

   -  ``-1`` means the default device in the selected platform

   -  **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details

-  ``gpu_use_dp`` , default = ``false``, type = bool

   -  set this to ``true`` to use double precision math on GPU (by default single precision is used)

.. end params list

Others
------

Continued Training with Input Score

LightGBM supports continued training with initial scores. It uses an additional file to store these initial scores, like the following:

::

0.5
-0.1
0.9
...

It means the initial score of the first data row is 0.5, second is -0.1, and so on. The initial score file corresponds with data file line by line, and has per score per line.

And if the name of data file is train.txt, the initial score file should be named as train.txt.init and placed in the same folder as the data file. In this case, LightGBM will auto load initial score file if it exists.

Weight Data


LightGBM supports weighted training. It uses an additional file to store weight data, like the following:

::

    1.0
    0.5
    0.8
    ...

It means the weight of the first data row is ``1.0``, second is ``0.5``, and so on.
The weight file corresponds with data file line by line, and has per weight per line.

And if the name of data file is ``train.txt``, the weight file should be named as ``train.txt.weight`` and placed in the same folder as the data file.
In this case, LightGBM will load the weight file automatically if it exists.

Also, you can include weight column in your data file. Please refer to the ``weight_column`` `parameter <#weight_column>`__ in above.

Query Data
~~~~~~~~~~

For learning to rank, it needs query information for training data.
LightGBM uses an additional file to store query data, like the following:

::

    27
    18
    67
    ...

It means first ``27`` lines samples belong to one query and next ``18`` lines belong to another, and so on.

**Note**: data should be ordered by the query.

If the name of data file is ``train.txt``, the query file should be named as ``train.txt.query`` and placed in the same folder as the data file.
In this case, LightGBM will load the query file automatically if it exists.

Also, you can include query/group id column in your data file. Please refer to the ``group_column`` `parameter <#group_column>`__ in above.

.. _Laurae++ Interactive Documentation: https://sites.google.com/view/lauraepp/parameters

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