## 4 Evaluation
### Parameter Selection  
As the decision boundary may assume any real value between 0 and 1, it is pragmatic to restrict the available actions to incremental steps \((\mu)\) of 0.03. Accordingly, the range 0 < a < 0.5 applies for \(Li\), and 0.5 < a < 1 applies for \(Uli\), ensuring a suitable number of possible actions without overcomplicating the search space.  
To assess our contextual bandit-based reinforcement learning approach's proficiency in minimizing detection costs, we employed nine distinct reward policies. Each policy operates on a unique reward scale, thus complicating direct comparisons using metrics such as mean reward and cumulative regret. We used a grid search strategy to tune key parameters across these policies. Specifically, for each policy, we ran a systematic exploration of parameter combinations, using 50 iterations per configuration.  
Initially, we explored various parameter settings over 50 iterations for each policy. This phase focused on two key metrics: (a) the total count of triaged observations, and (b) the residual number of erroneous predictions. Each configuration was evaluated using a hold-out validation subset (20% of the dataset), and tuning was performed independently for each dataset-model pair.  
Our approach further examines the hypothesis that combining reward policies can exploit their individual strengths, thereby enhancing overall performance. For instance, we first integrated the contextual index with distance, aiming to interpret the relevance of each action relative to the cumulative probability distribution of model predictions. We also investigated the combination of imbalanced cost with distance, as reflected in policies such as \(\rho^{d_{p,A_{i}}}\), \(d_{p,A_{i}}^{p}\), and \(\log\left(\rho^{d_{p,A_{i}}}\right)\).  
The parameter tuning involved varying both the decay constant \((\gamma)\) and the exploration term \((\tau)\). The Softmax and UCB strategies were implemented following the update rules outlined in Section 3. For \(\tau\), we tested values [0.1, 0.2, 0.4, 0.8, 1.0] in the Softmax function to explore trade-offs between exploration and exploitation. For the UCB family, \(\gamma\) was tuned across [0.1, 0.2, 0.4, 0.8, 1.0], determining the aggressiveness of exploration. The optimal configuration can be chosen based on the following priorities: (a) higher detection rates, (b) greater cost savings, or (c) a balanced trade-off. This study emphasizes configurations that favor cost-efficiency. Parameter selection was finalized based on the configuration that achieved the best F1-score under the lowest triage cost.  
Although the UCB-based variants exhibited the most comprehensive cost-effectiveness, we found that two of our proposed reward policies offer notable versatility when utilizing Softmax. To support robust selection, correlation heatmaps between false predictions and triage rates were analyzed per configuration. Correlation analyses indicated a strong relationship between residual false predictions and triage rates across various parameters and reward policies. In particular, for the parameters \(log(k)\) and \(log(d_{p,A,t}\times k)\), it is possible to select points along the correlation curve that cater to specific contextual requirements, rather than focusing exclusively on maximizing cost savings.