# 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.
Valid values: integer |

`num_round` |
The number of rounds to run the training.
Valid values: integer |

`alpha` |
L1 regularization term on weights. Increasing this value makes models more conservative.
Valid values: float Default value: 1 |

`base_score` |
The initial prediction score of all instances, global bias.
Valid values: float Default value: 0.5 |

`booster` |
Which booster to use. The
Valid values: String. One of Default value: |

`colsample_bylevel` |
Subsample ratio of columns for each split, in each level.
Valid values: Float. Range: [0,1]. Default value: 1 |

`colsample_bytree` |
Subsample ratio of columns when constructing each tree.
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.
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
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
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.
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.
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
Valid values: String. Either Default value: |

`lambda` |
L2 regularization term on weights. Increasing this value makes models more conservative.
Valid values: float Default value: 1 |

`lambda_bias` |
L2 regularization term on bias.
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
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.
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
Valid values: Integer. Range: [0,∞) Default value: 6 |

`max_leaves` |
Maximum number of nodes to be added. Relevant only if
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
Valid values: Float. Range: [0,∞). Default value: 1 |

`normalize_type` |
Type of normalization algorithm.
Valid values: Either Default value: |

`nthread` |
Number of parallel threads used to run
Valid values: integer Default value: Maximum number of threads. |

`objective` |
Specifies the learning task and the corresponding learning
objective. Examples:
Valid values: string Default value: |

`one_drop` |
When this flag is enabled, at least one tree is always dropped during the dropout.
Valid values: 0 or 1 Default value: 0 |

`process_type` |
The type of boosting process to run.
Valid values: String. Either Default value: |

`rate_drop` |
The dropout rate that specifies the fraction of previous trees to drop during the dropout.
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
Valid values: 0/1 Default value: 1 |

`sample_type` |
Type of sampling algorithm.
Valid values: Either Default value: |

`scale_pos_weight` |
Controls the balance of positive and negative weights. It's
useful for unbalanced classes. A typical value to consider:
Valid values: float Default value: 1 |

`seed` |
Random number seed.
Valid values: integer Default value: 0 |

`silent` |
0 means print running messages, 1 means silent mode. Valid values: 0 or 1
Default value: 0 |

`sketch_eps` |
Used only for approximate greedy algorithm. This translates
into O(1 /
Valid values: Float, Range: [0, 1]. Default value: 0.03 |

`skip_drop` |
Probability of skipping the dropout procedure during a boosting iteration.
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.
Valid values: Float. Range: [0,1]. Default value: 1 |

`tree_method` |
The tree construction algorithm used in XGBoost.
Valid values: One of Default value: |

`tweedie_variance_power` |
Parameter that controls the variance of the Tweedie distribution.
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.
Valid values: comma-separated string. Default value: |