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feature-engineering

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nni
featuretools
thehomebrewnerd
thehomebrewnerd commented Sep 20, 2021

The integration of Woodwork resulted in three lines in the EntitySet.__eq__ method that were previously covered by tests, now being uncovered. We should update the equality tests to make sure all conditions are properly covered by tests.

The conditions that are not covered are shown below:
![image.png](https://images.zenhubusercontent.com/5da89a3c8b7bd200019886b7/dbbaa7d5-9c2d-4b29-acbf-a0461af

adchia
adchia commented Sep 5, 2021

Currently, if you try to use BQ and materialize a feature that is a list (of numbers, strings, etc), Feast will crash because in BQ, the value type of the feature is a dictionary, such as

{'list': [{'item': 3}, {'item': 3}]}
In materialize, we convert the latest values retrieval job to a pyarrow table and then converts to ValueProtos to write. This calls

`python_type_to_feast_value_type

evalml
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
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