Features and Feature Stores

Features Are the Design Surface

In predictive ML interviews, explain which features the model needs and whether they will be available when a prediction is made.

Good features can matter as much as model choice. Compare models and feature sets through evaluation rather than assuming a more complex model will help.

Use specific examples and explain each feature's source, freshness, and cost.

Here is a useful habit. Generate features in four groups, and say the group names out loud. The examples below are for the ETA problem, which is predicting delivery time at checkout.

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