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While two of our 4-index models are an order of magnitude faster than existing models on 22 test problems of varying sizes, we find that no single model performs consistently the best for all problems.
In both examples, the RLC model performs consistently with expectations.
The Rosner Colditz model performs consistently well when applied in an independent data set.
If the model performs consistently well in all external datasets, then all IPD studies can be used for model development.
Through validation in an independent data set, we have shown that the Rosner Colditz model performs consistently when applied in that independent setting.
We presented these findings under the heading of "over-fitting", but in light of the reviewers' comments we clarified this in the section renamed "The model performs consistently across different subsets of the data".
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The model performed consistently well (AUC > 0.9), significantly better than random when we compared predicted deforestation risk to observed.
However, the demographic model performed consistently better at predicting the presence of at least one breeding pair in local landscapes.
The comprehensive model performed consistently better across all three metrics considered when the training models estimated using the SEER cohort was applied to the cohorts from MBRCC and the Director's Challenge study, with the results being consistent across histology in the MBRCC cohort.
The model performed consistently across a range of cancers, including rare cancers.
Taken all these results together, it can be speculated that our model performed consistently well, and high accuracy is not due to over optimization.
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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