## 3 Results
### Analysis of the Results
Some key observations that arise from these results are as follows: the fact that the partial churn predictions align with the full churn outcomes seems to suggest that full churners are a subset of partial churners. This relationship skews the performance metrics, especially the F1 scores and recall, which are lower for full-churn predictions, while the AUC remains relatively stable across full and partial churn scenarios. Additionally, the artificial test settings show slightly inflated metrics compared to the true tests, which further underlines the need for real application conditions for robust model evaluation. While the AUC stays rather constant across the full and partial churn scenarios, other metrics, such as F1 score and recall, are sensitive to temporal gaps. Therefore, to further enhance the performance and reliability of the model, we recommend integrating dynamic model adaptation techniques for evolving customer behaviors. Investigation into cost-sensitive learning methods would probably allow for a better balance between precision and recall to be obtained, particularly in partial churn cases. Furthermore, additional experiments using smaller time windows or other datasets could validate these findings under different conditions. Figure 8: Precision. Figure 7: Recall. In the \"Partial Train-Artificial Test-Partial Churn\" and \"Full Train-True Test-Partial Churn\" curves, the only difference was in the test data used. Figure 4 represents the most important metric F1 score. A similar pattern can be seen in terms of delay--the artificial test curve, in some instances, repeats the behavior of the true test curve with delay (we will try to measure this later). This could potentially indicate that the prediction success might depend more on the time windows on which it is tested than on the time windows on which it is trained. Therefore, the results of our experiments led us to formulate the following two hypotheses:  
**Hypothesis 1**. : _The shift drift is less pronounced if we map the evaluations of predictions to the windows the predictions were applied to, rather than to the windows of the actual data on which the model was trained._  
**Hypothesis 2**. : _Full churn can be predicted using training data with partial churn labels without a significant drop in performance if the threshold value for prediction is modified._  
As for the first hypotheses, we tried to investigate further. To take a closer look at the mentioned two curves, we considered an alternative view: instead of using the modeling moment on the horizontal axis, we used the time window the model used for testing. The result is represented by the dotted line in Figure 9.  
The second hypothesis relies on the AUC metrics being almost identical for both churn definitions--the AUC is the only metric that does not depend on the threshold value for classification. This fact strongly supports this hypothesis, although we could not fully prove it. Investigating the Proposed Hypotheses: Time-Window Shifts and Threshold Tuning  
In order to find evidence supporting the first hypothesis, we first shifted the time window for true testing scenarios backward by 40 days. This can be seen as a change in the interpretation of the horizontal axis--now, it represents the time windows the model is being tested on, rather than the time window on which it was trained. Figure 9 shows both the original F1 scores for the \"Partial Train-True Test-Partial Churn\" and \"Partial Train-Artificial Test-Partial Churn\" scenarios and the shifted view of the first one (dotted line). Examining the three curves--Partial Train-True Test-Partial Churn, \"Partial Train-Artificial Test-Partial Churn\", and \"Partial Train-Artificial Test-Partial Churn (Shifted -40)\"--we can observe that shifting the testing moments reduces the difference between the curves, as measured by Equation (4). Specifically, the difference between the curves without  
Figure 9: F1 score for different testing time windows vs. modeling moment time windows.