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feature-engineering

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nni
achals
achals commented Jul 7, 2021

Expected Behavior

When an entity is removed from the feature repo, it should be removed from the feature registry.

Current Behavior

Entities are only added, never removed from the Registry. This is a storage leak of sorts.

Steps to reproduce

  • feast init
  • In the new feature repo, feast apply
  • Add a new entity in the feature repo. feast apply.
  • Remove the new en
evalml
angela97lin
angela97lin commented Oct 6, 2021

Right now, component_graph.get_component expects a string which is the unique name used to find a component in the graph (ex: "My Label Encoder", and not "Label Encoder"). This makes it difficult to find components of a specific class, ex LabelEncoder instances. We should add a method to help with this!

API should take into consideration what happens if we have multiple of the same component

Hyperactive

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

  • Updated Nov 29, 2020
  • Jupyter Notebook

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