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In the training as well as in the test period, the empirical setup is structured as follows.
Our study compares all performance metrics results across the same empirical setup, data preprocessing and attribute selectors.
It is defined in Eq. 2. Our study compares results across the same empirical setup, data preprocessing and attribute selectors.
In regards to the prediction accuracy, our approach allows to compare results across different models applying the same empirical setup and data preprocessing.
Based on this empirical setup, however, we are not able to completely quantify the general (labor market) equilibrium effects of the Flexjob scheme, such as any potential displacement of non-subsidized workers.
The advantage of our empirical setup is the clear time-line and its simplicity.
Similar(52)
Additionally, the results of different studies are difficult to compare due to different empirical setups and data preprocessing, possibly leading to contradictory results.
In addition, the results of different studies are difficult to compare due to their different empirical setups, data preprocessing, and dataset characteristics (Shepperd and MacDonell 2012; Langdon et al. 2016).
Ligand structures with semi-empirical charges were setup similarly as described above, except that in the last step PM6 charges were calculated using MOPAC2009 software.
Regarding the low power of this study as well as the fact that the setup mixed empirical with data-mining data, it is important to interpret our results with care.
In the first mixed-model analysis (Model 1 in Table 1) we explored the effects of F on the study setup, SS: empirical or modelling (Formula 1).
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