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By using validation and coping statements rather than reassurance we are helping to support that person's confidence and resilience.
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In this study, comparisons of assay performance between the different FDA-approved HIV-1 RNA assay platforms (both new and existing) were performed by using validation data that included both well-characterized virus stock and locally collected clinical samples.
This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets.
A suitable and practical procedure for assessing the trueness and precision of the proposed method has been applied by using validation standards.
Evaluation of the models' performance was based on statistical indices obtained from a confusion matrix by using validation samples.
The image was classified by using validation data collected during a six-week field study in 2008.
General steps (Fig. 1) include: (1) pre-quantitative analysis for target compositions, collecting and analyzing sample measurement sets by NIRS; (2) establishing a quantitative model for target compositions and then optimizing the model by NIRS; and (3) using validation sets to verify the accuracy and repeatability of the model.
For the purpose of model comparison, we calculate pseudo R 2, and the out-of-sample pseudo R 2 with bootstrapped confidence intervals by using cross-validation and bootstrap resampling as described above.
Cross-validation is a statistical technique of splitting data into training and validation sets by using the validation set to evaluate prediction ability of the trained model.
By using formal validation procedures and considering many different experimental settings, we show that our results are statistically significant and robust.
At the end, the quality of all three models was again assessed by the previously used validation servers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com