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A sobering, deeply influential observation.
Like Moore's Law, the significance of Koomey's Law is more as an influential observation than a scientific discovery.
We called this patient sample an "influential observation".
The inclusion of the influential observation worsens the performance of the signature.
One influential observation, which likely resulted from haze in the remotely-sensed data, was evident across all models.
However, a clear picture emerges by seeing the performance RMSE results of Fig. 2 in the validation set when the influential observation is omitted (n = 12).
Similar(38)
Regression diagnostics identified influential observations, overdispersion, or collinearity, and we observed no collinearity or overdispersion.
We found no evidence of poor model fit or influential observations after performing the following regression diagnostics: the Hosmer Lemeshow test, comparison of observed and predicted outcomes within exposure and covariate strata, examination of Pearson and deviance residual plots, and examination of leverage plots (Hosmer and Lemeshow 2000).
Goodness-of-fit test and regression diagnostics were performed for influential observations.
Application of this criterion led to the omission of 13 influential observations, reducing our sample to 400 observations.
This reduces the impact of outliers (influential observations) in the response space, providing a better fit for the majority of observations.
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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