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Case B: Up-to-date batch-wise external instances.
This figure shows the schematics of external instances and external features.
When more external instances are added, we see a divergence in behavior for Figure 6.
This indicates the presence of an optimum number of external instances for those price points.
For price-points above the aforementioned threshold, there is a monotonic increase in NPV upon adding more external instances.
This shows that above a certain price point, the addition of external instances is no longer feasible.
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Case C: One-time external instance dump.
For price points at or above $0.25, the clear answer to the optimum amount of external instance data is 50% of the incoming instances.
Cases A through D. In case of external data instances, the instances are only taken into account for model development and not for prediction.
If the amount of external data instances is fixed at say 0.2 times the test instances, then only those rows are relevant to this analysis.
We use both models for our experiments where external data instances are added.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com