# RCF Hyperparameters
<a name="rcf_hyperparameters"></a>

In the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) request, you specify the training algorithm. You can also specify algorithm-specific hyperparameters as string-to-string maps. The following table lists the hyperparameters for the Amazon SageMaker AI RCF algorithm. For more information, including recommendations on how to choose hyperparameters, see [How RCF Works](rcf_how-it-works.md).




| Parameter Name | Description | 
| --- | --- | 
| feature\$1dim |  The number of features in the data set. (If you use the [Random Cut Forest](https://sagemaker.readthedocs.io/en/stable/algorithms/unsupervised/randomcutforest.html) estimator, this value is calculated for you and need not be specified.) **Required** Valid values: Positive integer (min: 1, max: 10000)  | 
| eval\$1metrics |  A list of metrics used to score a labeled test data set. The following metrics can be selected for output: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/rcf_hyperparameters.html) **Optional** Valid values: a list with possible values taken from `accuracy` or `precision_recall_fscore`.  Default value: Both `accuracy`, `precision_recall_fscore` are calculated.  | 
| num\$1samples\$1per\$1tree |  Number of random samples given to each tree from the training data set. **Optional** Valid values: Positive integer (min: 1, max: 2048) Default value: 256  | 
| num\$1trees |  Number of trees in the forest. **Optional** Valid values: Positive integer (min: 50, max: 1000) Default value: 100  |