In the CreateTrainingJob 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.
| Parameter Name | Description |
|---|---|
feature_dim |
The number of features in the data set. (If you use the
Random Cut Forest Required Valid values: Positive integer (min: 1, max: 10000) |
eval_metrics |
A list of metrics used to score a labeled test data set. The following metrics can be selected for output:
Optional Valid values: a list with possible values taken from
Default value: Both |
num_samples_per_tree |
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_trees |
Number of trees in the forest. Optional Valid values: Positive integer (min: 50, max: 1000) Default value: 100 |
