# AutoGluon-Tabular hyperparameters The following table contains the subset of hyperparameters that are required or most commonly used for the Amazon SageMaker AI AutoGluon-Tabular algorithm. Users set these parameters to facilitate the estimation of model parameters from data. The SageMaker AI AutoGluon-Tabular algorithm is an implementation of the open-source [AutoGluon-Tabular](https://github.com/awslabs/autogluon) package. **Note** The default hyperparameters are based on example datasets in the [AutoGluon-Tabular sample notebooks](autogluon-tabular.md#autogluon-tabular-sample-notebooks). By default, the SageMaker AI AutoGluon-Tabular algorithm automatically chooses an evaluation metric based on the type of classification problem. The algorithm detects the type of classification problem based on the number of labels in your data. For regression problems, the evaluation metric is root mean squared error. For binary classification problems, the evaluation metric is area under the receiver operating characteristic curve (AUC). For multiclass classification problems, the evaluation metric is accuracy. You can use the `eval_metric` hyperparameter to change the default evaluation metric. Refer to the following table for more information on AutoGluon-Tabular hyperparameters, including descriptions, valid values, and default values. | Parameter Name | Description | | --- | --- | | eval\$1metric | The evaluation metric for validation data. If `eval_metric` is set to the default `"auto"` value, then the algorithm automatically chooses an evaluation metric based on the type of classification problem: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/autogluon-tabular-hyperparameters.html) Valid values: string, refer to the [AutoGluon documentation](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) for valid values. Default value: `"auto"`. | | presets | List of preset configurations for various arguments in `fit()`. [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/autogluon-tabular-hyperparameters.html) For more details, see [AutoGluon Predictors](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html). Valid values: string, any of the following: (`"best_quality"`, `"high_quality"`, `good_quality"`, `"medium_quality"`, `"optimize_for_deployment"`,` or "interpretable"`). Default value: `"medium_quality"`. | | auto\$1stack | Whether AutoGluon should automatically utilize bagging and multi-layer stack ensembling to boost predictive accuracy. Set `auto_stack` to `"True"` if you are willing to tolerate longer training times in order to maximize predictive accuracy. This automatically sets the `num_bag_folds` and `num_stack_levels` arguments based on dataset properties. Valid values: string, `"True"` or `"False"`. Default value: `"False"`. | | num\$1bag\$1folds | Number of folds used for bagging of models. When `num_bag_folds` is equal to `k`, training time is roughly increased by a factor of `k`. Set `num_bag_folds` to 0 to deactivate bagging. This is disabled by default, but we recommend using values between 5 and 10 to maximize predictive performance. Increasing `num_bag_folds` results in models with lower bias, but that are more prone to overfitting. One is an invalid value for this parameter, and will raise a `ValueError`. Values greater than 10 may produce diminishing returns and can even harm overall results due to overfitting. To further improve predictions, avoid increasing `num_bag_folds` and instead increase `num_bag_sets`. Valid values: string, any integer between (and including) `"0"` and `"10"`. Default value: `"0"`. | | num\$1bag\$1sets | Number of repeats of kfold bagging to perform (values must be greater than or equal to 1). The total number of models trained during bagging is equal to `num_bag_folds` \$1 `num_bag_sets`. This parameter defaults to one if `time_limit` is not specified. This parameters is disabled if `num_bag_folds` is not specified. Values greater than one result in superior predictive performance, especially on smaller problems and with stacking enabled. Valid values: integer, range: [`1`, `20`]. Default value: `1`. | | num\$1stack\$1levels | Number of stacking levels to use in stack ensemble. Roughly increases model training time by factor of `num_stack_levels` \$1 1. Set this parameter to 0 to deactivate stack ensembling. This parameter is deactivated by default, but we recommend using values between 1 and 3 to maximize predictive performance. To prevent overfitting and a `ValueError`, `num_bag_folds` must be greater than or equal to 2. Valid values: float, range: [`0`, `3`]. Default value: `0`. | | refit\$1full | Whether or not to retrain all models on all of the data (training and validation) after the normal training procedure. For more details, see [AutoGluon Predictors](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html). Valid values: string, `"True"` or `"False"`. Default value: `"False"`. | | set\$1best\$1to\$1refit\$1full | Whether or not to change the default model that the predictor uses for prediction. If `set_best_to_refit_full` is set to `"True"`, the default model changes to the model that exhibited the highest validation score as a result of refitting (activated by `refit_full`). Only valid if `refit_full` is set. Valid values: string, `"True"` or `"False"`. Default value: `"False"`. | | save\$1space | Whether or note to reduce the memory and disk size of predictor by deleting auxiliary model files that aren’t needed for prediction on new data. This has no impact on inference accuracy. We recommend setting `save_space` to `"True"` if the only goal is to use the trained model for prediction. Certain advanced functionality may no longer be available if `save_space` is set to `"True"`. Refer to the `[predictor.save\$1space()](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.save_space.html)` documentation for more details. Valid values: string, `"True"` or `"False"`. Default value: `"False"`. | | verbosity | The verbosity of print messages. `verbosity` levels range from `0` to `4`, with higher levels corresponding to more detailed print statements. A `verbosity` of `0` suppresses warnings. Valid values: integer, any of the following: (`0`, `1`, `2`, `3`, or `4`). Default value: `2`. |