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I am stucked in getting this block of statement.In my serving sets there are some anomalies.

I am not getting what this below code is doing in removing the anomalies.

payer_code = tfdv.get_feature(schema, 'payer_code')
payer_code.distribution_constraints.min_domain_mass = 0.9
Taniya Sisodia
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  • Can you explain yourself more? do you want to remove the constraint so you dont get domain anomalies ? or you what to detect the anomalies that you talking about ? and if so, what are those anomalies? – Amine_h Sep 26 '22 at 14:10

1 Answers1

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These lines of code relax the minimum fraction of values that must come from the domain for the feature payer-code. Basically it allows you to tolerate a fraction of missing values in your serving datasets vs your training schema, therefore considering previously detected anomalies as valid values.

jerem_y
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