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Finally, an aspect that should be taken into account in using learning machines to support visual quality assessment is that the loss function based on which the models in are optimized typically measures the prediction error (i.e., the difference between the predicted quality score and the actual human quality score).
The evaluation was focused on two measures: the prediction error expressed by an integrated Brier score (IBS), and the ranked measure of predictive ability calculated as a time-truncated concordance index (C index).
The RMSE measures the prediction consistency; smaller value means better performance.
We next develop a new metric that measures the prediction accuracy.
The second criterion is the Spearman rank-order correlation coefficient (SROCC) which measures the prediction monotonicity of an IQA approach.
Since this Table measures the prediction performance of our machine learning method, we excluded all data for validation compounds from the respective training sets.
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We also measured the prediction accuracy of iTriplet in predicting transcription factor binding sites in E. Coli.
In the description of the different strategies, we have seen that a relevant aspect of validating the added predictive value of an omics score is how to measure the prediction accuracy of a prognostic rule.
Mean absolute error method is used to measure the prediction performance of context-aware multi-criteria recommenders by analysing the difference error between the predicted score and the score which is given by user.
The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates.
These kinds of individual differences are used as residuals and to measure the prediction errors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com