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The constant population model performed worst for both PS and SS estimators (Table 3).
For all reference models, except the CART reference model, the CART model performed worst (Table 2).The data hungriness of the various modelling techniques is reflected by the first part of the learning curves with <100 events per variable.
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The model performs worst in June.
This indicates that the model performs worst when it has to reconstruct more complicated topologies.
The TSM model performs worst since it applies histogram equalization to enhance the restoration obtained by TV regularization in the previous step.
Overall, we found that the strict clock model performs worst, and the t values obtained under the clock vary substantially between sub-schemes.
Sunshine duration-based models performed best, and cloud cover-based models performed worst.
For the zero flow ratio, the index model was best and the rainfall runoff modelling performed worst.
For both measures, the Gamma Log model performed the worst, with a RMSE twice the value of the RMSE for Square-root Normal model and with confidence intervals that are outside the bounds of all other models' BCIs.
The LASSO model (AUC 0.62; 95% C.I. 0.53 0.8) performed worst of all models and required 8 variables.
Although the NBR-based model was the most accurate, TCWetness and NDVI-based models showed similar results, while TCGreenness performed worst.
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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