[ML] specifying missing_field_value value and using it instead of empty_string #53108
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benwtrent
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…ty_string (elastic#53108) For analytics, we need a consistent way of indicating when a value is missing. Inheriting from anomaly detection, analysis sent `""` when a field is missing. This works fine with numbers, but the underlying analytics process actually treats `""` as a category in categorical values. Consequently, you end up with this situation in the resulting model ``` { "frequency_encoding" : { "field" : "RainToday", "feature_name" : "RainToday_frequency", "frequency_map" : { "" : 0.009844409027270245, "No" : 0.6472019970785184, "Yes" : 0.6472019970785184 } } } ``` For inference this is a problem, because inference will treat missing values as `null`. And thus not include them on the infer call against the model. This PR takes advantage of our new `missing_field_value` option and supplies `\0` as the value.
benwtrent
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…ty_string (#53108) (#53165) For analytics, we need a consistent way of indicating when a value is missing. Inheriting from anomaly detection, analysis sent `""` when a field is missing. This works fine with numbers, but the underlying analytics process actually treats `""` as a category in categorical values. Consequently, you end up with this situation in the resulting model ``` { "frequency_encoding" : { "field" : "RainToday", "feature_name" : "RainToday_frequency", "frequency_map" : { "" : 0.009844409027270245, "No" : 0.6472019970785184, "Yes" : 0.6472019970785184 } } } ``` For inference this is a problem, because inference will treat missing values as `null`. And thus not include them on the infer call against the model. This PR takes advantage of our new `missing_field_value` option and supplies `\0` as the value.
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For analytics, we need a consistent way of indicating when a value is missing. Inheriting from anomaly detection, analysis sent
""when a field is missing. This works fine with numbers, but the underlying analytics process actually treats""as a category in categorical values.Consequently, you end up with this situation in the resulting model
For inference this is a problem, because inference will treat missing values as
null. And thus not include them on the infer call against the model.This PR takes advantage of our new
missing_field_valueoption added in elastic/ml-cpp#1034 and supplies the default value,\0.