RCF Hyperparameters - Amazon SageMaker AI

RCF Hyperparameters

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 estimator, this value is calculated for you and need not be specified.)

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:

  • accuracy - returns fraction of correct predictions.

  • precision_recall_fscore - returns the positive and negative precision, recall, and F1-scores.

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_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