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More complex models achieved good calibration after 300 days of data.
Cross-validation suggested that all models achieved good CCC agreement between predicted and observed cases, with low RMSE values (table 2).
For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful.
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These studies have shown that the probabilistic models achieve good performance for identity matching.
The HOGHOFMBH models achieve good localization performance despite being trained on a small number of example sequences.
Finally the predicted value of the neural network was compared with the measured value by automatic optical level, which showed that the prediction model achieved good accuracy, and could be accepted in the engineering application.
The hypothesised model achieved good overall fit as indicated by a RMSEA of 0.030.
The model achieved good overall fit as indicated by a RMSEA of 0.030 900% CI 0.028 to 0.033).
This model achieved good performance characteristics including an area under the curve (AUC) of 0.80 (95% CI 0.73 to 0.86).
Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75%% accuracy for 19 drugs).
After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males.
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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