Edit an imported dataset for use in experiments
Imported datasets are configured automatically by the platform, but you can edit each version to make them more perfect for your experiment.
Before you can use a dataset version to run an experiment, you need to save your changes manually.
You are can’t edit an already saved version of the dataset, so you will need to duplicate it if you want to make further changes.
Edit and inspect datasets
Dataset features
You can inspect your dataset either in the Features view or in the Table view. Just toggle the switch.
Features view to inspect features
Each row displays the name of a feature, its shape, and the distribution of values.
Click on the wrench icon 
to view or update the feature settings.
Table view to inspect features
Each column displays the name of a feature, its shape, and the distribution of values. You can also see a preview of the values for the first examples.
Click on the wrench icon to view or update the feature settings.
Note that to speed up processing, the platform only analyzes a fraction of the examples contained in a dataset. This has two consequences:
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The information presented has a small degree of approximation.
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The shape and the encodings available for a feature are determined only from the analyzed examples.
Feature sets
Group features into feature sets, making several features available as a single input or target in your model.
Subsets
A subset allows you to reduce the size of your dataset, and to split the examples into disjoint sets for the purpose of training, validation, and testing.
Create and edit your subsets in Advanced settings mode.
You always need to have at least two subsets, one for training and one for validating the model.
Incompatible examples
Every example must have its features compatible with the shape and encoding indicated in the dataset’s version, or running your experiment will fail.
If your experiment fails because of incompatible examples, you can click the icon under the experiment’s name to get more information about the problematic examples.
Clean dataset before upload
If you want to check and clean your data before upload, you can use our DataCleaner tool.
