Platform For AI:XGboost Train

Tab

Parameter

Type

Description

Field Setting

labelCol

String

The label column.

featureCols

String array

The table-formatted feature columns. The values of the featureCols and vectorCol parameters are mutually exclusive. The input data must be of the table type.

vectorCol

String

The LibSVM-formatted vector column. The values of featureCols and vectorCol parameters are mutually exclusive. The input data must be of the LibSVM type.

weightCol

String

The name of the weight column.

set the model file path

String

The Object Storage Service (OSS) bucket path in which the model is stored.

Parameter Setting

The number of rounds for boosting

Integer

The number of training rounds.

objective

String

The learning task and the corresponding learning objective. Default value: binary:logistic.

Base score

Floating-point number

The global bias, which is the initial prediction score of all instances. Default value: 0.5.

The number of classes

Integer

The number of classes.

Tree Method

String

The tree construction algorithm. Default value: auto. Valid values:

• auto

• exact

• approx

• hist

L1 regularization term on weights

Floating-point number

The L1 regularization term on weights. Default value: 0.0.

L2 regularization term on weights

Floating-point number

The L2 regularization term on weights. Default value: 1.0.

eta

Floating-point number

The learning rate. Default value: 0.3.

scale_pos_weight

Floating-point number

The balance between positive and negative weights. Default value: 1.0.

sketch_eps

Floating-point number

You can use this parameter to control the number of bins. This parameter is available only if you set the Tree Method parameter to approx. Default value: 0.03.

Maximum number of discrete bins to bucket continuous features

Integer

The maximum number of discrete bins to bucket continuous features. This parameter is available only if you set the Tree Method parameter to hist. Default value: 256.

Maximum depth of a tree

Integer

The maximum depth of a tree. Default value: 6.

Max leaves

Integer

The maximum number of leaf nodes that you want to add. Default value: 0.

Min child weight

Floating-point number

The minimum weight required in a child node. Default value: 1.0.

Max delta step

Floating-point number

The maximum delta step allowed for a leaf node. This parameter allows you to adjust the granularity of the model. Default value: 0.0.

Subsample ratio of the training instances

Floating-point number

The subsample ratio of the training instances. Default value: 1.

Sampling method

String

The method used to sample the training instances. Default value: GRADIENT_BASED. Valid values:

• GRADIENT_BASED

• UNIFORM

Subsample ratio of columns for each level

Floating-point number

The subsample ratio of columns for each level. Default value: 1.0.

Subsample ratio of columns for each node (split)

Floating-point number

The subsample ratio of columns for each node. Default value: 1.0.

Subsample ratio of columns when constructing each tree

Floating-point number

The subsample ratio of columns when each tree is constructed. Default value: 1.0.

Grow Policy

String

The method used to add new nodes to the tree. Default value: depthwise. Valid values:

• depthwise

• lossguide

gamma

Floating-point number

The minimum loss reduction required to make a subsequent partition on a leaf node of the tree. Default value: 0.0.

Interaction constraints

String

The groups of variables that are allowed to interact.

Monotone constraints

String

The monotonicity constraints of a feature.

Tweedie variance power

Floating-point number

The variance of the Tweedie distribution. This parameter is valid only in the Tweedie distribution. Default value: 1.5.

Execution Tuning

Number of Workers

Positive integer

The number of worker nodes. This parameter must be used together with the Memory per worker, unit MB parameter. Valid values: [1, 9999].

Memory per worker, unit MB

Positive integer

The memory size of each worker node. Unit: MB. Valid values: [1024, 64 × 1024].