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Frequently Asked Questions

How do I install scikit-learn?

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip: The documentation includes more detailed installation instructions. See the changelog for a history of notable changes to scikit-learn. We welcome new contributors of all experience levels.

What is the sklearnprocessor?

The SKLearnProcessor handles Amazon SageMaker processing tasks for jobs using scikit-learn. framework_version ( str) – The version of scikit-learn. role ( str) – An AWS IAM role name or ARN. The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts.

What is sklearnmodel in scikit?

See SKLearnModel () for full details. An Scikit-learn SageMaker Model that can be deployed to a SageMaker Endpoint. Initialize an SKLearnModel. model_data ( str) – The S3 location of a SageMaker model data .tar.gz file. role ( str) – An AWS IAM role (either name or full ARN).

Is there a documentation for tslearn?

This package builds on (and hence depends on) scikit-learn, numpy and scipy libraries. This documentation contains a quick-start guide (including installation procedure and basic usage of the toolkit ), a complete API Reference, as well as a gallery of examples. Finally, if you use tslearn in a scientific publication, we would appreciate citations.


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