PCA 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 PCA training algorithm provided by
Amazon SageMaker. For more information about how PCA works, see How PCA Works.
| Parameter Name | Description |
|---|---|
feature_dim |
Input dimension. Required Valid values: positive integer |
mini_batch_size |
Number of rows in a mini-batch. Required Valid values: positive integer |
num_components |
The number of principal components to compute. Required Valid values: positive integer |
algorithm_mode |
Mode for computing the principal components. Optional Valid values: regular or randomized Default value: regular |
extra_components |
As the value increases, the solution becomes more accurate but the
runtime and memory consumption increase linearly. The default, -1,
means the maximum of 10 and Optional Valid values: Non-negative integer or -1 Default value: -1 |
subtract_mean |
Indicates whether the data should be unbiased both during training and at inference. Optional Valid values: One of true or false Default value: true |
