Amazon SageMaker
Developer Guide

XGBoost Hyperparameters

The Amazon SageMaker XGBoost algorithm is an implementation of the open-source XGBoost package. Currently Amazon SageMaker supports version 0.72. For more detail about hyperparameter configuration for this version of XGBoost, see Release 0.72 XGBoost Parameters.

Parameter Name Description
num_class

The number of classes.

Required if objective is set to multi:softmax or multi:softprob.

Valid values: integer

num_round

The number of rounds to run the training.

Required

Valid values: integer

alpha

L1 regularization term on weights. Increasing this value makes models more conservative.

Optional

Valid values: float

Default value: 1

base_score

The initial prediction score of all instances, global bias.

Optional

Valid values: float

Default value: 0.5

booster

Which booster to use. The gbtree and dart values use a tree-based model, while gblinear uses a linear function.

Optional

Valid values: String. One of gbtree, gblinear, or dart.

Default value: gbtree

colsample_bylevel

Subsample ratio of columns for each split, in each level.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

colsample_bytree

Subsample ratio of columns when constructing each tree.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

csv_weights

When this flag is enabled, XGBoost differentiates the importance of instances for csv input by taking the second column (the column after labels) in training data as the instance weights.

Optional

Valid values: 0 or 1

Default value: 0

early_stopping_rounds

The model trains until the validation score stops improving. Validation error needs to decrease at least every early_stopping_rounds to continue training. Amazon SageMaker hosting uses the best model for inference.

Optional

Valid values: integer

Default value: -

eta

Step size shrinkage used in updates to prevent overfitting. After each boosting step, you can directly get the weights of new features. The eta parameter actually shrinks the feature weights to make the boosting process more conservative.

Optional

Valid values: Float. Range: [0,1].

Default value: 0.3

eval_metric

Evaluation metrics for validation data. A default metric is assigned according to the objective:

para>
  • rmse: for regression

  • error: for classification

  • map: for ranking

For a list of valid inputs, see XGBoost Parameters.

Optional

Valid values: string

Default value: Default according to objective.

gamma

Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger, the more conservative the algorithm is.

Optional

Valid values: Float. Range: [0,∞).

Default value: 0

grow_policy

Controls the way that new nodes are added to the tree. Currently supported only if tree_method is set to hist.

Optional

Valid values: String. Either depthwise or lossguide.

Default value: depthwise

lambda

L2 regularization term on weights. Increasing this value makes models more conservative.

Optional

Valid values: float

Default value: 1

lambda_bias

L2 regularization term on bias.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0

max_bin

Maximum number of discrete bins to bucket continuous features. Used only if tree_method is set to hist.

Optional

Valid values: integer

Default value: 256

max_delta_step

Maximum delta step allowed for each tree's weight estimation. When a positive integer is used, it helps make the update more conservative. The preferred option is to use it in logistic regression. Set it to 1-10 to help control the update.

Optional

Valid values: Integer. Range: [0,∞).

Default value: 0

max_depth

Maximum depth of a tree. Increasing this value makes the model more complex and likely to be overfitted. 0 indicates no limit. A limit is required when grow_policy=depth-wise.

Optional

Valid values: Integer. Range: [0,∞)

Default value: 6

max_leaves

Maximum number of nodes to be added. Relevant only if grow_policy is set to lossguide.

Optional

Valid values: integer

Default value: 0

min_child_weight

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, the building process gives up further partitioning. In linear regression models, this simply corresponds to a minimum number of instances needed in each node. The larger the algorithm, the more conservative it is.

Optional

Valid values: Float. Range: [0,∞).

Default value: 1

normalize_type

Type of normalization algorithm.

Optional

Valid values: Either tree or forest.

Default value: tree

nthread

Number of parallel threads used to run xgboost.

Optional

Valid values: integer

Default value: Maximum number of threads.

objective

Specifies the learning task and the corresponding learning objective. Examples: reg:linear, reg:logistic, multi:softmax. For a full list of valid inputs, please refer to XGBoost Parameters.

Optional

Valid values: string

Default value: reg:linear

one_drop

When this flag is enabled, at least one tree is always dropped during the dropout.

Optional

Valid values: 0 or 1

Default value: 0

process_type

The type of boosting process to run.

Optional

Valid values: String. Either default or update.

Default value: default

rate_drop

The dropout rate that specifies the fraction of previous trees to drop during the dropout.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0.0

refresh_leaf

This is a parameter of the 'refresh' updater plugin. When set to true (1), tree leaves and tree node stats are updated. When set to false(0), only tree node stats are updated.

Optional

Valid values: 0/1

Default value: 1

sample_type

Type of sampling algorithm.

Optional

Valid values: Either uniform or weighted.

Default value: uniform

scale_pos_weight

Controls the balance of positive and negative weights. It's useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases).

Optional

Valid values: float

Default value: 1

seed

Random number seed.

Optional

Valid values: integer

Default value: 0

silent

0 means print running messages, 1 means silent mode.

Valid values: 0 or 1

Optional

Default value: 0

sketch_eps

Used only for approximate greedy algorithm. This translates into O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.

Optional

Valid values: Float, Range: [0, 1].

Default value: 0.03

skip_drop

Probability of skipping the dropout procedure during a boosting iteration.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0.0

subsample

Subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly collects half of the data instances to grow trees. This prevents overfitting.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

tree_method

The tree construction algorithm used in XGBoost.

Optional

Valid values: One of auto, exact, approx, or hist.

Default value: auto

tweedie_variance_power

Parameter that controls the variance of the Tweedie distribution.

Optional

Valid values: Float. Range: (1, 2).

Default value: 1.5

updater

A comma-separated string that defines the sequence of tree updaters to run. This provides a modular way to construct and to modify the trees.

For a full list of valid inputs, please refer to XGBoost Parameters.

Optional

Valid values: comma-separated string.

Default value: grow_colmaker, prune