XGBoost Parameters

Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters.

  • General parameters relate to which booster we are using to do boosting, commonly tree or linear model

  • Booster parameters depend on which booster you have chosen

  • Learning task parameters decide on the learning scenario. For example, regression tasks may use different parameters with ranking tasks.

  • Command line parameters relate to behavior of CLI version of XGBoost.


Parameters in R package

In R-package, you can use . (dot) to replace underscore in the parameters, for example, you can use max.depth to indicate max_depth. The underscore parameters are also valid in R.

Global Configuration

The following parameters can be set in the global scope, using xgboost.config_context() (Python) or xgb.set.config() (R).

  • verbosity: Verbosity of printing messages. Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug).

  • use_rmm: Whether to use RAPIDS Memory Manager (RMM) to allocate GPU memory. This option is only applicable when XGBoost is built (compiled) with the RMM plugin enabled. Valid values are true and false.

General Parameters

  • booster [default= gbtree ]

    • Which booster to use. Can be gbtree, gblinear or dart; gbtree and dart use tree based models while gblinear uses linear functions.

  • device [default= cpu]

    Added in version 2.0.0.

    • Device for XGBoost to run. User can set it to one of the following values:

      • cpu: Use CPU.

      • cuda: Use a GPU (CUDA device).

      • cuda:<ordinal>: <ordinal> is an integer that specifies the ordinal of the GPU (which GPU do you want to use if you have more than one devices).

      • gpu: Default GPU device selection from the list of available and supported devices. Only cuda devices are supported currently.

      • gpu:<ordinal>: Default GPU device selection from the list of available and supported devices. Only cuda devices are supported currently.

      For more information about GPU acceleration, see XGBoost GPU Support. In distributed environments, ordinal selection is handled by distributed frameworks instead of XGBoost. As a result, using cuda:<ordinal> will result in an error. Use cuda instead.

  • verbosity [default=1]

    • Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug). Sometimes XGBoost tries to change configurations based on heuristics, which is displayed as warning message. If there’s unexpected behaviour, please try to increase value of verbosity.

  • validate_parameters [default to false, except for Python, R and CLI interface]

    • When set to True, XGBoost will perform validation of input parameters to check whether a parameter is used or not. A warning is emitted when there’s unknown parameter.

  • nthread [default to maximum number of threads available if not set]

    • Number of parallel threads used to run XGBoost. When choosing it, please keep thread contention and hyperthreading in mind.

  • disable_default_eval_metric [default= false]

    • Flag to disable default metric. Set to 1 or true to disable.

Parameters for Tree Booster

  • eta [default=0.3, alias: learning_rate]

    • Step size shrinkage used in update to prevent overfitting. After each boosting step, we can directly get the weights of new features, and eta shrinks the feature weights to make the boosting process more conservative.

    • range: [0,1]

  • gamma [default=0, alias: min_split_loss]

    • Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be. Note that a tree where no splits were made might still contain a single terminal node with a non-zero score.

    • range: [0,∞]

  • max_depth [default=6]

    • Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree. exact tree method requires non-zero value.

    • range: [0,∞]

  • min_child_weight [default=1]

    • Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression task, this simply corresponds to minimum number of instances needed to be in each node. The larger min_child_weight is, the more conservative the algorithm will be.

    • range: [0,∞]

  • max_delta_step [default=0]

    • Maximum delta step we allow each leaf output to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update.

    • range: [0,∞]

  • subsample [default=1]

    • Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing trees. and this will prevent overfitting. Subsampling will occur once in every boosting iteration.

    • range: (0,1]

  • sampling_method [default= uniform]

    • The method to use to sample the training instances.

    • uniform: each training instance has an equal probability of being selected. Typically set subsample >= 0.5 for good results.

    • gradient_based: the selection probability for each training instance is proportional to the regularized absolute value of gradients (more specifically, \(\sqrt{g^2+\lambda h^2}\)). subsample may be set to as low as 0.1 without loss of model accuracy. Note that this sampling method is only supported when tree_method is set to hist and the device is cuda; other tree methods only support uniform sampling.

  • colsample_bytree, colsample_bylevel, colsample_bynode [default=1]

    • This is a family of parameters for subsampling of columns.

    • All colsample_by* parameters have a range of (0, 1], the default value of 1, and specify the fraction of columns to be subsampled.

    • colsample_bytree is the subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.

    • colsample_bylevel is the subsample ratio of columns for each level. Subsampling occurs once for every new depth level reached in a tree. Columns are subsampled from the set of columns chosen for the current tree.

    • colsample_bynode is the subsample ratio of columns for each node (split). Subsampling occurs once every time a new split is evaluated. Columns are subsampled from the set of columns chosen for the current level. This is not supported by the exact tree method.

    • colsample_by* parameters work cumulatively. For instance, the combination {'colsample_bytree':0.5, 'colsample_bylevel':0.5, 'colsample_bynode':0.5} with 64 features will leave 8 features to choose from at each split.

      Using the Python or the R package, one can set the feature_weights for DMatrix to define the probability of each feature being selected when using column sampling. There’s a similar parameter for fit method in sklearn interface.

  • lambda [default=1, alias: reg_lambda]

    • L2 regularization term on weights. Increasing this value will make model more conservative.

    • range: [0, \(\infty\)]

  • alpha [default=0, alias: reg_alpha]

    • L1 regularization term on weights. Increasing this value will make model more conservative.

    • range: [0, \(\infty\)]

  • tree_method string [default= auto]

    • The tree construction algorithm used in XGBoost. See description in the reference paper and Tree Methods.

    • Choices: auto, exact, approx, hist, this is a combination of commonly used updaters. For other updaters like refresh, set the parameter updater directly.

      • auto: Same as the hist tree method.

      • exact: Exact greedy algorithm. Enumerates all split candidates.

      • approx: Approximate greedy algorithm using quantile sketch and gradient histogram.

      • hist: Faster histogram optimized approximate greedy algorithm.

  • scale_pos_weight [default=1]

    • Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative instances) / sum(positive instances). See Parameters Tuning for more discussion. Also, see Higgs Kaggle competition demo for examples: R, py1, py2, py3.

  • updater

    • A comma separated string defining the sequence of tree updaters to run, providing a modular way to construct and to modify the trees. This is an advanced parameter that is usually set automatically, depending on some other parameters. However, it could be also set explicitly by a user. The following updaters exist:

      • grow_colmaker: non-distributed column-based construction of trees.

      • grow_histmaker: distributed tree construction with row-based data splitting based on global proposal of histogram counting.

      • grow_quantile_histmaker: Grow tree using quantized histogram.

      • grow_gpu_hist: Enabled when tree_method is set to hist along with device=cuda.

      • grow_gpu_approx: Enabled when tree_method is set to approx along with device=cuda.

      • sync: synchronizes trees in all distributed nodes.

      • refresh: refreshes tree’s statistics and/or leaf values based on the current data. Note that no random subsampling of data rows is performed.

      • prune: prunes the splits where loss < min_split_loss (or gamma) and nodes that have depth greater than max_depth.

  • refresh_leaf [default=1]

    • This is a parameter of the refresh updater. When this flag is 1, tree leafs as well as tree nodes’ stats are updated. When it is 0, only node stats are updated.

  • process_type [default= default]

    • A type of boosting process to run.

    • Choices: default, update

      • default: The normal boosting process which creates new trees.

      • update: Starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updaters is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iterations performed. Currently, the following built-in updaters could be meaningfully used with this process type: refresh, prune. With process_type=update, one cannot use updaters that create new trees.

  • grow_policy [default= depthwise]

    • Controls a way new nodes are added to the tree.

    • Currently supported only if tree_method is set to hist or approx.

    • Choices: depthwise, lossguide

      • depthwise: split at nodes closest to the root.

      • lossguide: split at nodes with highest loss change.

  • max_leaves [default=0]

    • Maximum number of nodes to be added. Not used by exact tree method.

  • max_bin, [default=256]

    • Only used if tree_method is set to hist or approx.

    • Maximum number of discrete bins to bucket continuous features.

    • Increasing this number improves the optimality of splits at the cost of higher computation time.

  • num_parallel_tree, [default=1]

    • Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.

  • monotone_constraints

  • interaction_constraints

    • Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nest list, e.g. [[0, 1], [2, 3, 4]], where each inner list is a group of indices of features that are allowed to interact with each other. See Feature Interaction Constraints for more information.

  • multi_strategy, [default = one_output_per_tree]

    Added in version 2.0.0.


    This parameter is working-in-progress.

    • The strategy used for training multi-target models, including multi-target regression and multi-class classification. See Multiple Outputs for more information.

      • one_output_per_tree: One model for each target.

      • multi_output_tree: Use multi-target trees.

  • max_cached_hist_node, [default = 65536]

    Maximum number of cached nodes for CPU histogram.

    Added in version 2.0.0.

    • For most of the cases this parameter should not be set except for growing deep trees on CPU.

Parameters for Categorical Feature

These parameters are only used for training with categorical data. See Categorical Data for more information.


These parameters are experimental. exact tree method is not yet supported.

  • max_cat_to_onehot

    Added in version 1.6.0.

    • A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes.

  • max_cat_threshold

    Added in version 1.7.0.

    • Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting.

Additional parameters for Dart Booster (booster=dart)


Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set iteration_range to a nonzero value, e.g.

preds = bst.predict(dtest, iteration_range=(0, num_round))
  • sample_type [default= uniform]

    • Type of sampling algorithm.

      • uniform: dropped trees are selected uniformly.

      • weighted: dropped trees are selected in proportion to weight.

  • normalize_type [default= tree]

    • Type of normalization algorithm.

      • tree: new trees have the same weight of each of dropped trees.

        • Weight of new trees are 1 / (k + learning_rate).

        • Dropped trees are scaled by a factor of k / (k + learning_rate).

      • forest: new trees have the same weight of sum of dropped trees (forest).

        • Weight of new trees are 1 / (1 + learning_rate).

        • Dropped trees are scaled by a factor of 1 / (1 + learning_rate).

  • rate_drop [default=0.0]

    • Dropout rate (a fraction of previous trees to drop during the dropout).

    • range: [0.0, 1.0]

  • one_drop [default=0]

    • When this flag is enabled, at least one tree is always dropped during the dropout (allows Binomial-plus-one or epsilon-dropout from the original DART paper).

  • skip_drop [default=0.0]

    • Probability of skipping the dropout procedure during a boosting iteration.

      • If a dropout is skipped, new trees are added in the same manner as gbtree.

      • Note that non-zero skip_drop has higher priority than rate_drop or one_drop.

    • range: [0.0, 1.0]

Parameters for Linear Booster (booster=gblinear)

  • lambda [default=0, alias: reg_lambda]

    • L2 regularization term on weights. Increasing this value will make model more conservative. Normalised to number of training examples.

  • alpha [default=0, alias: reg_alpha]

    • L1 regularization term on weights. Increasing this value will make model more conservative. Normalised to number of training examples.

  • updater [default= shotgun]

    • Choice of algorithm to fit linear model

      • shotgun: Parallel coordinate descent algorithm based on shotgun algorithm. Uses ‘hogwild’ parallelism and therefore produces a nondeterministic solution on each run.

      • coord_descent: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. When the device parameter is set to cuda or gpu, a GPU variant would be used.

  • feature_selector [default= cyclic]

    • Feature selection and ordering method

      • cyclic: Deterministic selection by cycling through features one at a time.

      • shuffle: Similar to cyclic but with random feature shuffling prior to each update.

      • random: A random (with replacement) coordinate selector.

      • greedy: Select coordinate with the greatest gradient magnitude. It has O(num_feature^2) complexity. It is fully deterministic. It allows restricting the selection to top_k features per group with the largest magnitude of univariate weight change, by setting the top_k parameter. Doing so would reduce the complexity to O(num_feature*top_k).

      • thrifty: Thrifty, approximately-greedy feature selector. Prior to cyclic updates, reorders features in descending magnitude of their univariate weight changes. This operation is multithreaded and is a linear complexity approximation of the quadratic greedy selection. It allows restricting the selection to top_k features per group with the largest magnitude of univariate weight change, by setting the top_k parameter.

  • top_k [default=0]

    • The number of top features to select in greedy and thrifty feature selector. The value of 0 means using all the features.

Learning Task Parameters

Specify the learning task and the corresponding learning objective. The objective options are below:

  • objective [default=reg:squarederror]

    • reg:squarederror: regression with squared loss.

    • reg:squaredlogerror: regression with squared log loss \(\frac{1}{2}[log(pred + 1) - log(label + 1)]^2\). All input labels are required to be greater than -1. Also, see metric rmsle for possible issue with this objective.

    • reg:logistic: logistic regression, output probability

    • reg:pseudohubererror: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.

    • reg:absoluteerror: Regression with L1 error. When tree model is used, leaf value is refreshed after tree construction. If used in distributed training, the leaf value is calculated as the mean value from all workers, which is not guaranteed to be optimal.

      Added in version 1.7.0.

    • reg:quantileerror: Quantile loss, also known as pinball loss. See later sections for its parameter and Quantile Regression for a worked example.

      Added in version 2.0.0.

    • binary:logistic: logistic regression for binary classification, output probability

    • binary:logitraw: logistic regression for binary classification, output score before logistic transformation

    • binary:hinge: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.

    • count:poisson: Poisson regression for count data, output mean of Poisson distribution.

      • max_delta_step is set to 0.7 by default in Poisson regression (used to safeguard optimization)

    • survival:cox: Cox regression for right censored survival time data (negative values are considered right censored). Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function h(t) = h0(t) * HR).

    • survival:aft: Accelerated failure time model for censored survival time data. See Survival Analysis with Accelerated Failure Time for details.

    • multi:softmax: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)

    • multi:softprob: same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata * nclass matrix. The result contains predicted probability of each data point belonging to each class.

    • rank:ndcg: Use LambdaMART to perform pair-wise ranking where Normalized Discounted Cumulative Gain (NDCG) is maximized. This objective supports position debiasing for click data.

    • rank:map: Use LambdaMART to perform pair-wise ranking where Mean Average Precision (MAP) is maximized

    • rank:pairwise: Use LambdaRank to perform pair-wise ranking using the ranknet objective.

    • reg:gamma: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed.

    • reg:tweedie: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.

  • base_score

    • The initial prediction score of all instances, global bias

    • The parameter is automatically estimated for selected objectives before training. To disable the estimation, specify a real number argument.

    • If base_margin is supplied, base_score will not be added.

    • For sufficient number of iterations, changing this value will not have too much effect.

    See Intercept for more info.

  • eval_metric [default according to objective]

    • Evaluation metrics for validation data, a default metric will be assigned according to objective (rmse for regression, and logloss for classification, mean average precision for rank:map, etc.)

    • User can add multiple evaluation metrics. Python users: remember to pass the metrics in as list of parameters pairs instead of map, so that latter eval_metric won’t override previous ones

    • The choices are listed below:

      • rmse: root mean square error

      • rmsle: root mean square log error: \(\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}\). Default metric of reg:squaredlogerror objective. This metric reduces errors generated by outliers in dataset. But because log function is employed, rmsle might output nan when prediction value is less than -1. See reg:squaredlogerror for other requirements.

      • mae: mean absolute error

      • mape: mean absolute percentage error

      • mphe: mean Pseudo Huber error. Default metric of reg:pseudohubererror objective.

      • logloss: negative log-likelihood

      • error: Binary classification error rate. It is calculated as #(wrong cases)/#(all cases). For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.

      • error@t: a different than 0.5 binary classification threshold value could be specified by providing a numerical value through ‘t’.

      • merror: Multiclass classification error rate. It is calculated as #(wrong cases)/#(all cases).

      • mlogloss: Multiclass logloss.

      • auc: Receiver Operating Characteristic Area under the Curve. Available for classification and learning-to-rank tasks.

        • When used with binary classification, the objective should be binary:logistic or similar functions that work on probability.

        • When used with multi-class classification, objective should be multi:softprob instead of multi:softmax, as the latter doesn’t output probability. Also the AUC is calculated by 1-vs-rest with reference class weighted by class prevalence.

        • When used with LTR task, the AUC is computed by comparing pairs of documents to count correctly sorted pairs. This corresponds to pairwise learning to rank. The implementation has some issues with average AUC around groups and distributed workers not being well-defined.

        • On a single machine the AUC calculation is exact. In a distributed environment the AUC is a weighted average over the AUC of training rows on each node - therefore, distributed AUC is an approximation sensitive to the distribution of data across workers. Use another metric in distributed environments if precision and reproducibility are important.

        • When input dataset contains only negative or positive samples, the output is NaN. The behavior is implementation defined, for instance, scikit-learn returns \(0.5\) instead.

      • aucpr: Area under the PR curve. Available for classification and learning-to-rank tasks.

        After XGBoost 1.6, both of the requirements and restrictions for using aucpr in classification problem are similar to auc. For ranking task, only binary relevance label \(y \in [0, 1]\) is supported. Different from map (mean average precision), aucpr calculates the interpolated area under precision recall curve using continuous interpolation.

      • pre: Precision at \(k\). Supports only learning to rank task.

      • ndcg: Normalized Discounted Cumulative Gain

      • map: Mean Average Precision

        The average precision is defined as:

        \[AP@l = \frac{1}{min{(l, N)}}\sum^l_{k=1}P@k \cdot I_{(k)}\]

        where \(I_{(k)}\) is an indicator function that equals to \(1\) when the document at \(k\) is relevant and \(0\) otherwise. The \(P@k\) is the precision at \(k\), and \(N\) is the total number of relevant documents. Lastly, the mean average precision is defined as the weighted average across all queries.

      • ndcg@n, map@n, pre@n: \(n\) can be assigned as an integer to cut off the top positions in the lists for evaluation.

      • ndcg-, map-, ndcg@n-, map@n-: In XGBoost, the NDCG and MAP evaluate the score of a list without any positive samples as \(1\). By appending “-” to the evaluation metric name, we can ask XGBoost to evaluate these scores as \(0\) to be consistent under some conditions.

      • poisson-nloglik: negative log-likelihood for Poisson regression

      • gamma-nloglik: negative log-likelihood for gamma regression

      • cox-nloglik: negative partial log-likelihood for Cox proportional hazards regression

      • gamma-deviance: residual deviance for gamma regression

      • tweedie-nloglik: negative log-likelihood for Tweedie regression (at a specified value of the tweedie_variance_power parameter)

      • aft-nloglik: Negative log likelihood of Accelerated Failure Time model. See Survival Analysis with Accelerated Failure Time for details.

      • interval-regression-accuracy: Fraction of data points whose predicted labels fall in the interval-censored labels. Only applicable for interval-censored data. See Survival Analysis with Accelerated Failure Time for details.

  • seed [default=0]

    • Random number seed. In the R package, if not specified, instead of defaulting to seed ‘zero’, will take a random seed through R’s own RNG engine.

  • seed_per_iteration [default= false]

    • Seed PRNG determnisticly via iterator number.

Parameters for Tweedie Regression (objective=reg:tweedie)

  • tweedie_variance_power [default=1.5]

    • Parameter that controls the variance of the Tweedie distribution var(y) ~ E(y)^tweedie_variance_power

    • range: (1,2)

    • Set closer to 2 to shift towards a gamma distribution

    • Set closer to 1 to shift towards a Poisson distribution.

Parameter for using Pseudo-Huber (reg:pseudohubererror)

  • huber_slope : A parameter used for Pseudo-Huber loss to define the \(\delta\) term. [default = 1.0]

Parameter for using Quantile Loss (reg:quantileerror)

  • quantile_alpha: A scalar or a list of targeted quantiles.

    Added in version 2.0.0.

Parameter for using AFT Survival Loss (survival:aft) and Negative Log Likelihood of AFT metric (aft-nloglik)

  • aft_loss_distribution: Probability Density Function, normal, logistic, or extreme.

Parameters for learning to rank (rank:ndcg, rank:map, rank:pairwise)

These are parameters specific to learning to rank task. See Learning to Rank for an in-depth explanation.

  • lambdarank_pair_method [default = topk]

    How to construct pairs for pair-wise learning.

    • mean: Sample lambdarank_num_pair_per_sample pairs for each document in the query list.

    • topk: Focus on top-lambdarank_num_pair_per_sample documents. Construct \(|query|\) pairs for each document at the top-lambdarank_num_pair_per_sample ranked by the model.

  • lambdarank_num_pair_per_sample [range = \([1, \infty]\)]

    It specifies the number of pairs sampled for each document when pair method is mean, or the truncation level for queries when the pair method is topk. For example, to train with ndcg@6, set lambdarank_num_pair_per_sample to \(6\) and lambdarank_pair_method to topk.

  • lambdarank_normalization [default = true]

    Added in version 2.1.0.

    Whether to normalize the leaf value by lambda gradient. This can sometimes stagnate the training progress.

  • lambdarank_unbiased [default = false]

Specify whether do we need to debias input click data.

  • lambdarank_bias_norm [default = 2.0]

    \(L_p\) normalization for position debiasing, default is \(L_2\). Only relevant when lambdarank_unbiased is set to true.

  • ndcg_exp_gain [default = true]

    Whether we should use exponential gain function for NDCG. There are two forms of gain function for NDCG, one is using relevance value directly while the other is using \(2^{rel} - 1\) to emphasize on retrieving relevant documents. When ndcg_exp_gain is true (the default), relevance degree cannot be greater than 31.

Command Line Parameters

The following parameters are only used in the console version of XGBoost

  • num_round

    • The number of rounds for boosting

  • data

    • The path of training data

  • test:data

    • The path of test data to do prediction

  • save_period [default=0]

    • The period to save the model. Setting save_period=10 means that for every 10 rounds XGBoost will save the model. Setting it to 0 means not saving any model during the training.

  • task [default= train] options: train, pred, eval, dump

    • train: training using data

    • pred: making prediction for test:data

    • eval: for evaluating statistics specified by eval[name]=filename

    • dump: for dump the learned model into text format

  • model_in [default=NULL]

    • Path to input model, needed for test, eval, dump tasks. If it is specified in training, XGBoost will continue training from the input model.

  • model_out [default=NULL]

    • Path to output model after training finishes. If not specified, XGBoost will output files with such names as 0003.model where 0003 is number of boosting rounds.

  • model_dir [default= models/]

    • The output directory of the saved models during training

  • fmap

    • Feature map, used for dumping model

  • dump_format [default= text] options: text, json

    • Format of model dump file

  • name_dump [default= dump.txt]

    • Name of model dump file

  • name_pred [default= pred.txt]

    • Name of prediction file, used in pred mode

  • pred_margin [default=0]

    • Predict margin instead of transformed probability