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Fundamentals of ML observability
Metrics, feature importance and more
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The direct inputs to models are called features. For example, a patient diagnosis model might have features such as age, gender, one hot encoding of state, etc.
Features are usually numbers, because that is what most ML libraries know how to work with.
