PCA Hyperparameters - Amazon SageMaker AI (original) (raw)
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 AI. 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 num_components. Valid for_randomized_ mode only. 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 |
How It Works
Inference Formats
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