Retrieve feature impact scores for features in a model.

Retrieve feature impact scores for features in a model.
Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. Elsewhere this technique is sometimes called 'Permutation Importance'.
The impactUnnormalized is how much worse the error metric score is when making predictions on this modified data. The impactNormalized is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is a redundant feature, i.e. once other features are considered it doesn't contribute much in addition, the redundantWith value is the name of feature that has the highest correlation with this feature.
If a feature impact job was previously submitted, this endpoint will return a response structured like {{"message": , "jobId": }} where jobId is the ID of the job that can be retrieved with :http:get:/api/v2/projects/(projectId)/jobs/(jobId)/.

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