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Differentiating between CBT and PPT in the models increased the variance in the models, indicating that assessment condition (CBT vs PPT) is not a useful predictor of student scores.
New secondary models increased the accuracy of predictions (5 to 45 °C), with Bf and Af for SGR, LPD, and MPD of 1.00, 1.06, and 1.00 and 1.14, 1.33, and 1.02, respectively.
Texture resolution and polygon counts for models increased; the Reach assault rifle is constructed of more polygons than an entire Marine character from Halo 3. The prequel concept also gave the art team an opportunity to redesign key enemies, weapons, and elements of the series.
Addition of so-called 'additional criteria' to these models increased the sensitivity to almost 100%.
Using genomic models increased the accuracy of prediction compared to traditional BLUP methods.
However, adding parameters for modeling rate variation to either of the two models increased the model likelihood tremendously.
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In addition, applying software quality models increase the customer satisfaction and decrease the quality cost.
Using such paths for building discriminative models increases the dimensionality of the input space without improving classification accuracy.
More covariates in the prediction models increase the computing times.
The refined traceability of individual members offered by AB models increases the usage of computational resources.
Therefore, testing across a large number of models increases the reliability of the results obtained through model comparison procedures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com