## 5 Application and Discussion of the Proposed Metric and Visualization
### Results of the Random forest Regression of the Different Data Sets  
The hypothesis implies that the accuracy of data-driven models decreases with decreasing data set completeness. However, reduced record completeness does not necessarily lead to losses in accuracy. In order to evaluate this, a random forest regression model is trained to data set A and B individually, comparing the coefficient of determination and the RMSE to the original data set (cf. Table 3).  
The random forest regression trained on data set B achieves a coefficient of determination of 0.97. This is a significant loss compared to the original data set. Furthermore, the RMSE increases to 0.045, indicating a loss in accuracy. The prediction of the random forest regression is not as precise as before. The original data set can represent the dependencies of flow, stroke, pressure and pressure difference accurately. However, the reduced data set completeness conceals parts of the dependencies causing losses in prediction accuracy.  
In contrast, data set A achieves an accuracy similar to the original data set. This demonstrates that a reduced record completeness to 75% does not influence the accuracy necessarily. The random deletion of observations does reduce the amount of available data. However, this leads to problems only when the remaining data set is so small that the dependencies cannot be learned anymore (data quality attribute Appropriate Amount of Data [15; 16]). In data set A, the utilization of the features space is not affected by the reduced record completeness, remaining the essential information regarding the dependencies of the variables in all sections and classes. Therefore, the accuracies of data set A and the original data set are equally high.  
Furthermore, the reduced data set completeness in data set B leads to a change of the feature importance. Since the pressure difference in data set B shows significant deficiencies in data set completeness, the random forest regression model shifts the feature importance to the variable stroke. Due to the missing data in pressure difference, the models need to focus on stroke for the prediction. The feature importance of stroke is increased from 0.926 to 0.970, while the feature importance of pressure difference is decreased from 0.073 to 0.028.  
These results prove the hypothesis claimed in Section 1. While a reduced record completeness does not influence the model accuracy, the reduced data set completeness causes significant losses in the coefficient of determination and RMSE. This emphasized the need for a detailed and careful assessment of the data quality, especially the data set completeness. Raising the awareness for data set completeness supports experts and data analysts in taken appropriate actions for efficient data modelling. On the one hand, further data can be acquired to prevent defects in data set completeness. On the other hand, specific methods can be used to prevent the damage of data quality defects. In case of data set completeness, approaches such as resampling could balance the data to ensure unbiased model training [59]. Furthermore, ensemble algorithms such as boosting, which combines several classifiers, can increase the validity of a model [60]. However, the identification and measuring of the data quality defects is a prerequisite to take appropriate countermeasures.