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.
181ee3ae0b