# xgboosttreenode properties #

![XGBoost Tree node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/pythonxgboosttreenodeicon.png)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\.

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xgboosttreenode properties  

Table 1\. xgboosttreenode properties

| `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`                    |           |                                                                                                                                                                                                                                                                                                                                                                                                |

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