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To evaluate the predictive performance of the classification method, we performed a cross-validation experiment using the gold standard compound protein interaction data.
In addition, we compared the predictive performance of the classification method with that of the direct method.
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However overall we have to mention that it seems that in some cases performance of the classification methods depends on the dataset and a specific method cannot be concluded as a best method.
We report the AUC, averaged over 500 experiments, as the overall performance measure of the classification method at hand.
We repeated the five-fold cross-validation for 100 random partitions of the given dataset, and averaged the resulting 500 AUCs to obtain a reliable performance measure of the classification method.
After the feature extraction stage, a k-fold cross validation based leave-one-out validation (LOOCV) technique is used for performance evaluation of the classification methods.
The performance of the proposed classification method is compared with six other similar methods.
Also, in order to evaluate the performance of the proposed classification method, it is compared with six other similar methods.
> This section provides details on the implementation and performance evaluation procedures for the comparison of the classification methods used in this study.
Nevertheless, our results clearly demonstrate (Table 2) that one of the main reasons for the controversial estimates of the diagnostic performance of light-based aids may be the classification method employed for the reference standard.
In both datasets, we replaced the missing spots log ratios by 0. In order to assess the performance of a classification method, we performed a cross-validation for each of the three classification problems, following a leave-one-out (LOO) procedure for the bladder dataset and a 10-fold procedure for the melanoma dataset.
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