Amazon SageMaker
Developer Guide

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 RCF algorithm. For more information, including recommendations on how to choose hyperparameters, see How RCF Works.

Parameter Name Description
num_samples_per_tree

Number of random samples given to each tree from the training data set.

Valid values: Positive integer (min: 1, max: 2048)

Default value: 256

num_trees

Number of trees in the forest.

Valid values: Positive integer (min: 50, max: 1000)

Default value: 100

feature_dim

Number of features in the data set.

Valid values: Positive integer (min: 1, max: 10000)

Default value: -

eval_metics

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.

Valid values: a list with possible values taken from accuracy, precision_recall_fscore.

Default value: accuracy, precision_recall_fscore