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Fig. 4 Performance comparison over different datasets.
The process has been repeated 10 times, and we report the average scores over different datasets.
Indeed, more than one project file can be calculated over different datasets.
The experiments have been repeated 10 times, and then we report the average scores over different datasets in Table 1.
By choosing a fixed classifier (1-NN), we can evaluate the classification performance of different distance metrics over different datasets.
To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets.
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The proposed approach is tested and compared with baseline Vold Kalman Filtering over four different datasets.
In addition, the obtained values are similar over the different datasets.
In order to perform the comparison, we apply the pairwise alignment pipeline over six different datasets, for a total of 99 RI pairs.
Table 2 Comparison between methods over six different datasets based on LOOCV for each drug, assuming no DTIs are known for each drug.
As an example, the data might be vertically partitioned (the attributes are spread over the different datasets), in which case multiple intermediate queries will have to be executed and of which the results should be merged.
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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