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Parameters

This page contains descriptions of all parameters in LightGBM.

List of other helpful links

Parameters Format

Parameters are merged together in the following order (later items overwrite earlier ones):

  1. LightGBM's default values
  2. special files for weight, init_score, query, and positions (see Others)
  3. (CLI only) configuration in a file passed like config=train.conf
  4. (CLI only) configuration passed via the command line
  5. (Python, R) special keyword arguments to some functions (e.g. num_boost_round in train())
  6. (Python, R) params function argument (including **kwargs in Python and ... in R)
  7. (C API) parameters or params function argument

Many parameters have "aliases", alternative names which refer to the same configuration.

Where a mix of the primary parameter name and aliases are given, the primary parameter name is always preferred to any aliases.

For example, in Python:

# use learning rate of 0.07, because 'learning_rate'
# is the primary parameter name
lgb.train(
   params={
      "learning_rate": 0.07,
      "shrinkage_rate": 0.12
   },
   train_set=dtrain
)

Where multiple aliases are given, and the primary parameter name is not, the first alias appearing in the lists returned by Config::parameter2aliases() in the C++ library is used. Those lists are hard-coded in a fairly arbitrary way... wherever possible, avoid relying on this behavior.

For example, in Python:

# use learning rate of 0.12, LightGBM has a hard-coded preference for 'shrinkage_rate'
# over any other aliases, and 'learning_rate' is not provided
lgb.train(
   params={
      "eta": 0.19,
      "shrinkage_rate": 0.12
   },
   train_set=dtrain
)

CLI

The parameters format is key1=value1 key2=value2 .... Parameters can be set both in config file and command line. By using command line, parameters should not have spaces before and after =. By using config files, one line can only contain one parameter. You can use # to comment.

Python

Any parameters that accept multiple values should be passed as a Python list.

params = {
   "monotone_constraints": [-1, 0, 1]
}

R

Any parameters that accept multiple values should be passed as an R list.

params <- list(
   monotone_constraints = c(-1, 0, 1)
)

Core Parameters

  • config 🔗︎, default = "", type = string, aliases: config_file
    • path of config file
    • Note: can be used only in CLI version
  • task 🔗︎, default = train, type = enum, options: train, predict, convert_model, refit, aliases: task_type
    • train, for training, aliases: training
    • predict, for prediction, aliases: prediction, test
    • convert_model, for converting model file into if-else format, see more information in Convert Parameters
    • refit, for refitting existing models with new data, aliases: refit_tree
    • save_binary, load train (and validation) data then save dataset to binary file. Typical usage: save_binary first, then run multiple train tasks in parallel using the saved binary file
    • Note: can be used only in CLI version; for language-specific packages you can use the correspondent functions
  • objective 🔗︎, default = regression, type = enum, options: regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg, aliases: objective_type, app, application, loss
    • regression application
      • regression, L2 loss, aliases: regression_l2, l2, mean_squared_error, mse, l2_root, root_mean_squared_error, rmse
      • regression_l1, L1 loss, aliases: l1, mean_absolute_error, mae
      • huber, Huber loss
      • fair, Fair loss
      • poisson, Poisson regression
      • quantile, Quantile regression
      • mape, MAPE loss, aliases: mean_absolute_percentage_error
      • 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
      • 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
    • binary classification application
      • binary, binary log loss classification (or logistic regression)
      • requires labels in {0, 1}; see cross-entropy application for general probability labels in [0, 1]
    • multi-class classification application
      • multiclass, softmax objective function, aliases: softmax
      • multiclassova, One-vs-All binary objective function, aliases: multiclass_ova, ova, ovr
      • num_class should be set as well
    • cross-entropy application
      • cross_entropy, objective function for cross-entropy (with optional linear weights), aliases: xentropy
      • cross_entropy_lambda, alternative parameterization of cross-entropy, aliases: xentlambda
      • label is anything in interval [0, 1]
    • ranking application
      • lambdarank, lambdarank objective. 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
      • rank_xendcg, XE_NDCG_MART ranking objective function, aliases: xendcg, xe_ndcg, xe_ndcg_mart, xendcg_mart
      • rank_xendcg is faster than and achieves the similar performance as lambdarank
      • label should be int type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
    • custom objective function (gradients and hessians not computed directly by LightGBM)
      • custom
      • must be passed through parameters explicitly in the C API
      • Note: cannot be used in CLI version
  • boosting 🔗︎, default = gbdt, type = enum, options: gbdt, rf, dart, aliases: boosting_type, boost
    • gbdt, traditional Gradient Boosting Decision Tree, aliases: gbrt
    • rf, Random Forest, aliases: random_forest
    • dart, Dropouts meet Multiple Additive Regression Trees
      • Note: internally, LightGBM uses gbdt mode for the first 1 / learning_rate iterations
  • data_sample_strategy 🔗︎, default = bagging, type = enum, options: bagging, goss
    • bagging, Randomly Bagging Sampling
      • Note: bagging is only effective when bagging_freq > 0 and bagging_fraction < 1.0
    • goss, Gradient-based One-Side Sampling
    • New in version 4.0.0
  • data 🔗︎, default = "", type = string, aliases: train, train_data, train_data_file, data_filename
    • path of training data, LightGBM will train from this data
    • Note: can be used only in CLI version
  • valid 🔗︎, default = "", type = string, aliases: test, valid_data, valid_data_file, test_data, test_data_file, valid_filenames
    • path(s) of validation/test data, LightGBM will output metrics for these data
    • support multiple validation data, separated by ,
    • Note: can be used only in CLI version
  • num_iterations 🔗︎, default = 100, type = int, aliases: num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, nrounds, num_boost_round, n_estimators, max_iter, constraints: num_iterations >= 0
    • number of boosting iterations
    • Note: internally, LightGBM constructs num_class * num_iterations trees for multi-class classification problems
  • learning_rate 🔗︎, default = 0.1, type = double, aliases: shrinkage_rate, eta, constraints: learning_rate > 0.0
    • shrinkage rate
    • in dart, it also affects on normalization weights of dropped trees
  • num_leaves 🔗︎, default = 31, type = int, aliases: num_leaf, max_leaves, max_leaf, max_leaf_nodes, constraints: 1 < num_leaves <= 131072
    • max number of leaves in one tree
  • tree_learner 🔗︎, default = serial, type = enum, options: serial, feature, data, voting, aliases: tree, tree_type, tree_learner_type
    • serial, single machine tree learner
    • feature, feature parallel tree learner, aliases: feature_parallel
    • data, data parallel tree learner, aliases: data_parallel
    • voting, voting parallel tree learner, aliases: voting_parallel
    • refer to Distributed Learning Guide to get more details
  • num_threads 🔗︎, default = 0, type = int, aliases: num_thread, nthread, nthreads, n_jobs
    • used only in train, prediction and refit tasks or in correspondent functions of language-specific packages
    • number of threads for LightGBM
    • 0 means default number of threads in OpenMP
    • for the best speed, set this to the number of real CPU cores, not the number of threads (most CPUs use hyper-threading to generate 2 threads per CPU core)
    • do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)
    • be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. This is normal
    • for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication
    • Note: please don't change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
  • device_type 🔗︎, default = cpu, type = enum, options: cpu, gpu, cuda, aliases: device
    • device for the tree learning
    • cpu supports all LightGBM functionality and is portable across the widest range of operating systems and hardware
    • cuda offers faster training than gpu or cpu, but only works on GPUs supporting CUDA
    • gpu can be faster than cpu and works on a wider range of GPUs than CUDA
    • Note: it is recommended to use the smaller max_bin (e.g. 63) to get the better speed up
    • Note: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set gpu_use_dp=true to enable 64-bit float point, but it will slow down the training
    • Note: refer to Installation Guide to build LightGBM with GPU or CUDA support
  • seed 🔗︎, default = None, type = int, aliases: random_seed, random_state
    • this seed is used to generate other seeds, e.g. data_random_seed, feature_fraction_seed, etc.
    • by default, this seed is unused in favor of default values of other seeds
    • this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly
  • deterministic 🔗︎, default = false, type = bool
    • used only with cpu device type
    • setting this to true should ensure the stable results when using the same data and the same parameters (and different num_threads)
    • when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different
    • you can raise issues in LightGBM GitHub repo when you meet the unstable results
    • Note: setting this to true may slow down the training
    • Note: to avoid potential instability due to numerical issues, please set force_col_wise=true or force_row_wise=true when setting deterministic=true

Learning Control Parameters

  • force_col_wise 🔗︎, default = false, type = bool
    • used only with cpu device type
    • set this to true to force col-wise histogram building
    • enabling this is recommended when:
      • the number of columns is large, or the total number of bins is large
      • num_threads is large, e.g. > 20
      • you want to reduce memory cost
    • Note: when both force_col_wise and force_row_wise are false, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to true manually
    • Note: this parameter cannot be used at the same time with force_row_wise, choose only one of them
  • force_row_wise 🔗︎, default = false, type = bool
    • used only with cpu device type
    • set this to true to force row-wise histogram building
    • enabling this is recommended when:
      • the number of data points is large, and the total number of bins is relatively small
      • num_threads is relatively small, e.g. <= 16
      • you want to use small bagging_fraction or goss sample strategy to speed up
    • Note: setting this to true will double the memory cost for Dataset object. If you have not enough memory, you can try setting force_col_wise=true
    • Note: when both force_col_wise and force_row_wise are false, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to true manually
    • Note: this parameter cannot be used at the same time with force_col_wise, choose only one of them
  • histogram_pool_size 🔗︎, default = -1.0, type = double, aliases: hist_pool_size
    • max cache size in MB for historical histogram
    • < 0 means no limit
  • max_depth 🔗︎, default = -1, type = int
    • limit the max depth for tree model. This is used to deal with over-fitting when #data is small. Tree still grows leaf-wise
    • <= 0 means no limit
  • min_data_in_leaf 🔗︎, default = 20, type = int, aliases: min_data_per_leaf, min_data, min_child_samples, min_samples_leaf, constraints: min_data_in_leaf >= 0
    • minimal number of data in one leaf. Can be used to deal with over-fitting
    • Note: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations
  • min_sum_hessian_in_leaf 🔗︎, default = 1e-3, type = double, aliases: min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight, constraints: min_sum_hessian_in_leaf >= 0.0
    • minimal sum hessian in one leaf. Like min_data_in_leaf, it can be used to deal with over-fitting
  • bagging_fraction 🔗︎, default = 1.0, type = double, aliases: sub_row, subsample, bagging, constraints: 0.0 < bagging_fraction <= 1.0
    • like feature_fraction, but this will randomly select part of data without resampling
    • can be used to speed up training
    • can be used to deal with over-fitting
    • Note: to enable bagging, bagging_freq should be set to a non zero value as well
  • pos_bagging_fraction 🔗︎, default = 1.0, type = double, aliases: pos_sub_row, pos_subsample, pos_bagging, constraints: 0.0 < pos_bagging_fraction <= 1.0
    • used only in binary application
    • used for imbalanced binary classification problem, will randomly sample #pos_samples * pos_bagging_fraction positive samples in bagging
    • should be used together with neg_bagging_fraction
    • set this to 1.0 to disable
    • Note: to enable this, you need to set bagging_freq and neg_bagging_fraction as well
    • Note: if both pos_bagging_fraction and neg_bagging_fraction are set to 1.0, balanced bagging is disabled
    • Note: if balanced bagging is enabled, bagging_fraction will be ignored
  • neg_bagging_fraction 🔗︎, default = 1.0, type = double, aliases: neg_sub_row, neg_subsample, neg_bagging, constraints: 0.0 < neg_bagging_fraction <= 1.0
    • used only in binary application
    • used for imbalanced binary classification problem, will randomly sample #neg_samples * neg_bagging_fraction negative samples in bagging
    • should be used together with pos_bagging_fraction
    • set this to 1.0 to disable
    • Note: to enable this, you need to set bagging_freq and pos_bagging_fraction as well
    • Note: if both pos_bagging_fraction and neg_bagging_fraction are set to 1.0, balanced bagging is disabled
    • Note: if balanced bagging is enabled, bagging_fraction will be ignored
  • bagging_freq 🔗︎, default = 0, type = int, aliases: subsample_freq
    • frequency for bagging
    • 0 means disable bagging; k means perform bagging at every k iteration. Every k-th iteration, LightGBM will randomly select bagging_fraction * 100% of the data to use for the next k iterations
    • Note: bagging is only effective when 0.0 < bagging_fraction < 1.0
  • bagging_seed 🔗︎, default = 3, type = int, aliases: bagging_fraction_seed
    • random seed for bagging
  • bagging_by_query 🔗︎, default = false, type = bool
    • whether to do bagging sample by query
  • feature_fraction 🔗︎, default = 1.0, type = double, aliases: sub_feature, colsample_bytree, constraints: 0.0 < feature_fraction <= 1.0
    • LightGBM will randomly select a subset of features on each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree
    • can be used to speed up training
    • can be used to deal with over-fitting
  • feature_fraction_bynode 🔗︎, default = 1.0, type = double, aliases: sub_feature_bynode, colsample_bynode, constraints: 0.0 < feature_fraction_bynode <= 1.0
    • LightGBM will randomly select a subset of features on each tree node if feature_fraction_bynode is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features at each tree node
    • can be used to deal with over-fitting
    • Note: unlike feature_fraction, this cannot speed up training
    • Note: if both feature_fraction and feature_fraction_bynode are smaller than 1.0, the final fraction of each node is feature_fraction * feature_fraction_bynode
  • feature_fraction_seed 🔗︎, default = 2, type = int
    • random seed for feature_fraction
  • extra_trees 🔗︎, default = false, type = bool, aliases: extra_tree
    • use extremely randomized trees
    • if set to true, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature
    • can be used to speed up training
    • can be used to deal with over-fitting
  • extra_seed 🔗︎, default = 6, type = int
    • random seed for selecting thresholds when extra_trees is true
  • early_stopping_round 🔗︎, default = 0, type = int, aliases: early_stopping_rounds, early_stopping, n_iter_no_change
    • will stop training if one metric of one validation data doesn't improve in last early_stopping_round rounds
    • <= 0 means disable
    • can be used to speed up training
  • early_stopping_min_delta 🔗︎, default = 0.0, type = double, constraints: early_stopping_min_delta >= 0.0
    • when early stopping is used (i.e. early_stopping_round > 0), require the early stopping metric to improve by at least this delta to be considered an improvement
    • New in version 4.4.0
  • first_metric_only 🔗︎, default = false, type = bool
    • LightGBM allows you to provide multiple evaluation metrics. Set this to true, if you want to use only the first metric for early stopping
  • max_delta_step 🔗︎, default = 0.0, type = double, aliases: max_tree_output, max_leaf_output
    • used to limit the max output of tree leaves
    • <= 0 means no constraint
    • the final max output of leaves is learning_rate * max_delta_step
  • lambda_l1 🔗︎, default = 0.0, type = double, aliases: reg_alpha, l1_regularization, constraints: lambda_l1 >= 0.0
    • L1 regularization
  • lambda_l2 🔗︎, default = 0.0, type = double, aliases: reg_lambda, lambda, l2_regularization, constraints: lambda_l2 >= 0.0
    • L2 regularization
  • linear_lambda 🔗︎, default = 0.0, type = double, constraints: linear_lambda >= 0.0
  • min_gain_to_split 🔗︎, default = 0.0, type = double, aliases: min_split_gain, constraints: min_gain_to_split >= 0.0
    • the minimal gain to perform split
    • can be used to speed up training
  • drop_rate 🔗︎, default = 0.1, type = double, aliases: rate_drop, constraints: 0.0 <= drop_rate <= 1.0
    • used only in dart
    • dropout rate: a fraction of previous trees to drop during the dropout
  • max_drop 🔗︎, default = 50, type = int
    • used only in dart
    • max number of dropped trees during one boosting iteration
    • <=0 means no limit
  • skip_drop 🔗︎, default = 0.5, type = double, constraints: 0.0 <= skip_drop <= 1.0
    • used only in dart
    • probability of skipping the dropout procedure during a boosting iteration
  • xgboost_dart_mode 🔗︎, default = false, type = bool
    • used only in dart
    • set this to true, if you want to use XGBoost DART mode
  • uniform_drop 🔗︎, default = false, type = bool
    • used only in dart
    • set this to true, if you want to use uniform drop
  • drop_seed 🔗︎, default = 4, type = int
    • used only in dart
    • random seed to choose dropping models
  • top_rate 🔗︎, default = 0.2, type = double, constraints: 0.0 <= top_rate <= 1.0
    • used only in goss
    • the retain ratio of large gradient data
  • other_rate 🔗︎, default = 0.1, type = double, constraints: 0.0 <= other_rate <= 1.0
    • used only in goss
    • the retain ratio of small gradient data
  • min_data_per_group 🔗︎, default = 100, type = int, constraints: min_data_per_group > 0
    • used for the categorical features
    • minimal number of data per categorical group
  • max_cat_threshold 🔗︎, default = 32, type = int, constraints: max_cat_threshold > 0
  • cat_l2 🔗︎, default = 10.0, type = double, constraints: cat_l2 >= 0.0
    • used for the categorical features
    • L2 regularization in categorical split
  • cat_smooth 🔗︎, default = 10.0, type = double, constraints: cat_smooth >= 0.0
    • used for the categorical features
    • this can reduce the effect of noises in categorical features, especially for categories with few data
  • max_cat_to_onehot 🔗︎, default = 4, type = int, constraints: max_cat_to_onehot > 0
    • used for the categorical features
    • when number of categories of one feature smaller than or equal to max_cat_to_onehot, one-vs-other split algorithm will be used
  • top_k 🔗︎, default = 20, type = int, aliases: topk, constraints: top_k > 0
    • used only in voting tree learner, refer to Voting parallel
    • set this to larger value for more accurate result, but it will slow down the training speed
  • monotone_constraints 🔗︎, default = None, type = multi-int, aliases: mc, monotone_constraint, monotonic_cst
    • used for constraints of monotonic features
    • 1 means increasing, -1 means decreasing, 0 means non-constraint
    • you need to specify all features in order. For example, mc=-1,0,1 means decreasing for the 1st feature, non-constraint for the 2nd feature and increasing for the 3rd feature
  • monotone_constraints_method 🔗︎, default = basic, type = enum, options: basic, intermediate, advanced, aliases: monotone_constraining_method, mc_method
    • used only if monotone_constraints is set
    • monotone constraints method
      • basic, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions
      • intermediate, a more advanced method, which may slow down the training speed very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
      • advanced, an even more advanced method, which may slow down the training speed. However, this method is even less constraining than the intermediate method and should again significantly improve the results
  • monotone_penalty 🔗︎, default = 0.0, type = double, aliases: monotone_splits_penalty, ms_penalty, mc_penalty, constraints: monotone_penalty >= 0.0
    • used only if monotone_constraints is set
    • monotone penalty: 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
    • if 0.0 (the default), no penalization is applied
  • feature_contri 🔗︎, default = None, type = multi-double, aliases: feature_contrib, fc, fp, feature_penalty
    • used to control feature's split gain, will use gain[i] = max(0, feature_contri[i]) * gain[i] to replace the split gain of i-th feature
    • you need to specify all features in order
  • forcedsplits_filename 🔗︎, default = "", type = string, aliases: fs, forced_splits_filename, forced_splits_file, forced_splits
    • path to a .json file that specifies splits to force at the top of every decision tree before best-first learning commences
    • .json file can be arbitrarily nested, and each split contains feature, threshold fields, as well as left and right fields representing subsplits
    • categorical splits are forced in a one-hot fashion, with left representing the split containing the feature value and right representing other values
    • Note: the forced split logic will be ignored, if the split makes gain worse
    • see this file as an example
  • refit_decay_rate 🔗︎, default = 0.9, type = double, constraints: 0.0 <= refit_decay_rate <= 1.0
    • decay rate of refit task, will use leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output to refit trees
    • used only in refit task in CLI version or as argument in refit function in language-specific package
  • cegb_tradeoff 🔗︎, default = 1.0, type = double, constraints: cegb_tradeoff >= 0.0
    • cost-effective gradient boosting multiplier for all penalties
  • cegb_penalty_split 🔗︎, default = 0.0, type = double, constraints: cegb_penalty_split >= 0.0
    • cost-effective gradient-boosting penalty for splitting a node
  • cegb_penalty_feature_lazy 🔗︎, default = 0,0,...,0, type = multi-double
    • cost-effective gradient boosting penalty for using a feature
    • applied per data point
  • cegb_penalty_feature_coupled 🔗︎, default = 0,0,...,0, type = multi-double
    • cost-effective gradient boosting penalty for using a feature
    • applied once per forest
  • path_smooth 🔗︎, default = 0, type = double, constraints: path_smooth >= 0.0
    • controls smoothing applied to tree nodes
    • helps prevent overfitting on leaves with few samples
    • if 0.0 (the default), no smoothing is applied
    • if path_smooth > 0 then min_data_in_leaf must be at least 2
    • larger values give stronger regularization
      • the weight of each node is w * (n / path_smooth) / (n / path_smooth + 1) + w_p / (n / path_smooth + 1), where n is the number of samples in the node, w is the optimal node weight to minimise the loss (approximately -sum_gradients / sum_hessians), and w_p is the weight of the parent node
      • note that the parent output w_p itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth
  • interaction_constraints 🔗︎, default = "", type = string
    • controls which features can appear in the same branch
    • by default interaction constraints are disabled, to enable them you can specify
      • for CLI, lists separated by commas, e.g. [0,1,2],[2,3]
      • for Python-package, list of lists, e.g. [[0, 1, 2], [2, 3]]
      • for R-package, list of character or numeric vectors, e.g. list(c("var1", "var2", "var3"), c("var3", "var4")) or list(c(1L, 2L, 3L), c(3L, 4L)). Numeric vectors should use 1-based indexing, where 1L is the first feature, 2L is the second feature, etc.
    • any two features can only appear in the same branch only if there exists a constraint containing both features
  • verbosity 🔗︎, default = 1, type = int, aliases: verbose
    • controls the level of LightGBM's verbosity
    • < 0: Fatal, = 0: Error (Warning), = 1: Info, > 1: Debug
  • input_model 🔗︎, default = "", type = string, aliases: model_input, model_in
    • filename of input model
    • for prediction task, this model will be applied to prediction data
    • for train task, training will be continued from this model
    • Note: can be used only in CLI version
  • output_model 🔗︎, default = LightGBM_model.txt, type = string, aliases: model_output, model_out
    • filename of output model in training
    • Note: can be used only in CLI version
  • saved_feature_importance_type 🔗︎, default = 0, type = int
    • the feature importance type in the saved model file
    • 0: count-based feature importance (numbers of splits are counted); 1: gain-based feature importance (values of gain are counted)
    • Note: can be used only in CLI version
  • snapshot_freq 🔗︎, default = -1, type = int, aliases: save_period
    • frequency of saving model file snapshot
    • set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if snapshot_freq=1
    • Note: can be used only in CLI version
  • use_quantized_grad 🔗︎, default = false, type = bool
    • whether to use gradient quantization when training
    • enabling this will discretize (quantize) the gradients and hessians into bins of num_grad_quant_bins
    • with quantized training, most arithmetics in the training process will be integer operations
    • gradient quantization can accelerate training, with little accuracy drop in most cases
    • Note: works only with cpu and cuda device type
    • New in version 4.0.0
  • num_grad_quant_bins 🔗︎, default = 4, type = int
    • used only if use_quantized_grad=true
    • number of bins to quantization gradients and hessians
    • with more bins, the quantized training will be closer to full precision training
    • Note: works only with cpu and cuda device type
    • New in version 4.0.0
  • quant_train_renew_leaf 🔗︎, default = false, type = bool
    • used only if use_quantized_grad=true
    • whether to renew the leaf values with original gradients when quantized training
    • renewing is very helpful for good quantized training accuracy for ranking objectives
    • Note: works only with cpu and cuda device type
    • New in version 4.0.0
  • stochastic_rounding 🔗︎, default = true, type = bool
    • used only if use_quantized_grad=true
    • whether to use stochastic rounding in gradient quantization
    • Note: works only with cpu and cuda device type
    • New in version 4.0.0

IO Parameters

Dataset Parameters

  • linear_tree 🔗︎, default = false, type = bool, aliases: linear_trees
    • fit piecewise linear gradient boosting tree
    • tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
    • the linear model at each leaf includes all the numerical features in that leaf's branch
    • the first tree has constant leaf values
    • categorical features are used for splits as normal but are not used in the linear models
    • missing values should not be encoded as 0. Use np.nan for Python, NA for the CLI, and NA, NA_real_, or NA_integer_ for R
    • it is recommended to rescale data before training so that features have similar mean and standard deviation
    • Note: works only with cpu, gpu device type and serial tree learner
    • Note: regression_l1 objective is not supported with linear tree boosting
    • Note: setting linear_tree=true significantly increases the memory use of LightGBM
    • Note: if you specify monotone_constraints, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
  • max_bin 🔗︎, default = 255, type = int, aliases: max_bins, 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 feature discrete bins
    • setting this to larger value will give better training result, but may increase data loading time
    • set this to larger value if data is very sparse
    • Note: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy
  • 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
  • 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 (the default) to tell LightGBM to ignore the features that are unsplittable based on 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 distributed 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 text 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 text 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
    • if omitted, the first column in the training data is used as the label
    • Note: works only in case of loading data directly from text 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 text 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
    • Note: weights should be non-negative
  • 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 text file
    • Note: data should be grouped by query_id, for more information, see Query Data
    • 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 text 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, categorical_features
    • 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: all values will be cast to int32 (integer codes will be extracted from pandas categoricals in the 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
    • Note: floating point numbers in categorical features will be rounded towards 0
  • 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 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
  • precise_float_parser 🔗︎, default = false, type = bool
    • use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input)
    • Note: setting this to true may lead to much slower text parsing
  • parser_config_file 🔗︎, default = "", type = string
    • path to a .json file that specifies customized parser initialized configuration
    • see lightgbm-transform for usage examples
    • Note: lightgbm-transform is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to issues page
    • New in version 4.0.0

Predict Parameters

  • start_iteration_predict 🔗︎, default = 0, type = int
    • used only in prediction task
    • used to specify from which iteration to start the prediction
    • <= 0 means from the first iteration
  • num_iteration_predict 🔗︎, default = -1, type = int
    • used only in prediction task
    • used to specify how many trained iterations will be used in prediction
    • <= 0 means no limit
  • predict_raw_score 🔗︎, default = false, type = bool, aliases: is_predict_raw_score, predict_rawscore, raw_score
    • used only in prediction task
    • set this to true to predict only the raw scores
    • set this to false to predict transformed scores
  • predict_leaf_index 🔗︎, default = false, type = bool, aliases: is_predict_leaf_index, leaf_index
    • used only in prediction task
    • set this to true to predict with leaf index of all trees
  • predict_contrib 🔗︎, default = false, type = bool, aliases: is_predict_contrib, contrib
    • used only in prediction task
    • set this to true to estimate SHAP values, which represent how each feature contributes to each prediction
    • produces #features + 1 values where the last value is the expected value of the model output over the training data
    • 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
    • Note: unlike the shap package, with predict_contrib we return a matrix with an extra column, where the last column is the expected value
    • Note: this feature is not implemented for linear trees
  • predict_disable_shape_check 🔗︎, default = false, type = bool
    • used only in prediction task
    • control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data
    • if false (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training
    • if true, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model
    • Note: be very careful setting this parameter to true
  • pred_early_stop 🔗︎, default = false, type = bool
    • used only in prediction task
    • used only in classification and ranking applications
    • used only for predicting normal or raw scores
    • if true, will use early-stopping to speed up the prediction. May affect the accuracy
    • Note: cannot be used with rf boosting type or custom objective function
  • pred_early_stop_freq 🔗︎, default = 10, type = int
    • used only in prediction task and if pred_early_stop=true
    • the frequency of checking early-stopping prediction
  • pred_early_stop_margin 🔗︎, default = 10.0, type = double
    • used only in prediction task and if pred_early_stop=true
    • the threshold of margin in early-stopping prediction
  • output_result 🔗︎, default = LightGBM_predict_result.txt, type = string, aliases: predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred
    • used only in prediction task
    • filename of prediction result
    • Note: can be used only in CLI version

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 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
  • fair_c 🔗︎, default = 1.0, type = double, constraints: fair_c > 0.0
    • used only in fair regression application
    • parameter for Fair loss
  • 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 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 = 30, type = int, constraints: lambdarank_truncation_level > 0
    • used only in lambdarank application
    • controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of LambdaMART paper
    • this parameter is closely related to the desirable cutoff k in the metric NDCG@k that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than k (e.g., k + 3) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric NDCG@k
  • 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 ,
  • lambdarank_position_bias_regularization 🔗︎, default = 0.0, type = double, constraints: lambdarank_position_bias_regularization >= 0.0
    • used only in lambdarank application when positional information is provided and position bias is modeled
    • larger values reduce the inferred position bias factors
    • New in version 4.1.0

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
      • mape, MAPE loss, aliases: mean_absolute_percentage_error
      • huber, Huber loss
      • fair, Fair loss
      • poisson, negative log-likelihood for 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, aliases: lambdarank, rank_xendcg, xendcg, xe_ndcg, xe_ndcg_mart, xendcg_mart
      • map, MAP, aliases: mean_average_precision
      • auc, AUC
      • average_precision, average precision score
      • binary_logloss, log loss, aliases: binary
      • binary_error, for one sample: 0 for correct classification, 1 for error classification
      • auc_mu, AUC-mu
      • 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, 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 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 distributed learning application
    • this parameter is needed to be set in both socket and MPI versions
  • local_listen_port 🔗︎, default = 12400 (random for Dask-package), 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 distributed learning application
    • each line contains one IP and one port for one machine. The format is ip port (space as a separator)
    • Note: can be used only in CLI version
  • 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
    • used only with gpu device type
    • OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform
    • -1 means the system-wide default platform
    • Note: refer to GPU Targets for more details
  • gpu_device_id 🔗︎, default = -1, type = int
    • OpenCL device ID in the specified platform or CUDA device ID. 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 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)
    • Note: can be used only in OpenCL implementation (device_type="gpu"), in CUDA implementation only double precision is currently supported
  • num_gpu 🔗︎, default = 1, type = int, constraints: num_gpu > 0
    • number of GPUs
    • Note: can be used only in CUDA implementation (device_type="cuda")

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.

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.

If binary data files exist for raw data file train.txt, for example in the name train.txt.bin, then the initial score file should be named as train.txt.bin.init.

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. Weights should be non-negative.

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 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
...

For wrapper libraries like in Python and R, this information can also be provided as an array-like via the Dataset parameter group.

[27, 18, 67, ...]

For example, if you have a 112-document dataset with group = [27, 18, 67], that means that you have 3 groups, where the first 27 records are in the first group, records 28-45 are in the second group, and records 46-112 are in the third group.

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 in above.