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The goodness-of-fit of both multivariate models was good, with a p value of 0.899 (Hosmer and Lemeshow test) for the model predicting pneumococcal pneumonia and a p value of 0.995 for the model predicting pneumonia of an atypical aetiology.
High coefficient of determination (R 2) values ( >99%) showed that the prediction ability of both the ANFIS and RSM models was good enough for the response when the interpolation ability of the models was considered.
The prediction discrimination of the 4 separate novel predictive models was good, with a c-index of 0.69 for ODI, 0.69 for EQ-5D, 0.67 for NRS-BP, and 0.64 for NRS-LP (i.e., good concordance between predicted outcomes and observed outcomes).
Kolmogorov-Smirnov's D values for CL-L3 vs Weibull for T1, T2 and T3 were 0.042 vs 0.049, 0.030 vs 0.035, 0.043 vs 0.048 respectively, but these differences are small and the fit of all models was good.
The fit of the path models was good.
The discriminative power of both the MLR and SVM models was good.
Similar(48)
The goodness-of-fit tests suggested that both models were good fits.
It proved that my models were good, and it repaid the trust that had been placed in me".
For example, although ML and related statistical models are good for prediction, they are not designed to estimate causal effects.
Phoneme-based models are good at capturing phonetic details.
The challenge is to know when the models are good enough.
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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