Last updated: Jan 17, 2024
XGBoost Tree© is an advanced implementation of a gradient
boosting algorithm with a tree model as the base model. Boosting algorithms iteratively learn weak
classifiers and then add them to a final strong classifier. XGBoost Tree is very flexible and
provides many parameters that can be overwhelming to most users, so the XGBoost Tree node in SPSS
Modeler exposes the core features and commonly used parameters. The node is implemented in
Python.
xgboosttreenode properties |
Data type | Property description |
|---|---|---|
custom_fields |
boolean | This option tells the node to use field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify the fields as required. |
target |
field | The target fields. |
inputs |
field | The input fields. |
tree_method
|
string | The tree method for model building. Possible values are auto,
exact, or approx. Default is auto. |
num_boost_round |
integer | The num boost round value for model building. Specify a value between 1 and
1000. Default is 10. |
max_depth |
integer | The max depth for tree growth. Specify a value of 1 or higher. Default is
6. |
min_child_weight |
Double | The min child weight for tree growth. Specify a value of 0 or higher.
Default is 1. |
max_delta_step |
Double | The max delta step for tree growth. Specify a value of 0 or higher. Default
is 0. |
objective_type |
string | The objective type for the learning task. Possible values are reg:linear,
reg:logistic, reg:gamma, reg:tweedie,
count:poisson, rank:pairwise, binary:logistic,
or multi. Note that for flag targets, only binary:logistic or
multi can be used. If multi is used, the score result will show
the multi:softmax and multi:softprob XGBoost objective
types. |
early_stopping |
Boolean | Whether to use the early stopping function. Default is False. |
early_stopping_rounds |
integer | Validation error needs to decrease at least every early stopping round(s) to continue
training. Default is 10. |
evaluation_data_ratio |
Double | Ration of input data used for validation errors. Default is 0.3. |
random_seed
|
integer | The random number seed. Any number between 0 and 9999999.
Default is 0. |
sample_size |
Double | The sub sample for control overfitting. Specify a value between 0.1 and
1.0. Default is 0.1. |
eta |
Double | The eta for control overfitting. Specify a value between 0 and
1. Default is 0.3. |
gamma |
Double | The gamma for control overfitting. Specify any number 0 or greater. Default
is 6. |
col_sample_ratio |
Double | The colsample by tree for control overfitting. Specify a value between 0.01
and 1. Default is 1. |
col_sample_level |
Double | The colsample by level for control overfitting. Specify a value between 0.01
and 1. Default is 1. |
lambda |
Double | The lambda for control overfitting. Specify any number 0 or greater. Default
is 1. |
alpha |
Double | The alpha for control overfitting. Specify any number 0 or greater. Default
is 0. |
scale_pos_weight |
Double | The scale pos weight for handling imbalanced datasets. Default is 1. |
use_HPO |