# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 5 Model Testing and Evaluation
### Model Evaluation  
This hybrid model test focuses on basic performance evaluation metrics, including _precision_, _recall_, and _F1 score_. These key indicators provide a deeper insight into the precision, recall and overall efficiency of the model in the context of analyzing responses, searching for keywords and generally its capabilities. By analyzing these metrics in detail, it is intended to provide a comprehensive picture of the model's performance to enable a relevant and informative assessment of its effectiveness. _Precision_ represents the ratio between the true positive predictions and all positive predictions of the model. This metric is valuable for assessing how accurate the model's responses are among all selected model predictions. High precision indicates that the model tends to produce correct results when it claims to know something. _Precision_ is defined as the ratio of true positives to the sum of true positives and false positives, expressed mathematically as:  
\[Precision=\frac{True\ positives}{True\ positives+False\ positives} \tag{25}\]  
This formula quantifies the accuracy of the model's predictions concerning positive instances. _Recall_ measures the ratio of true positive predictions to all true positive instances in the dataset. This metric highlights the model's ability to identify all relevant information, regardless of the total number of positives. A high _recall_ indicates that the model tends to capture the majority of relevant responses. _Recall_, also known as sensitivity or true positive rate, is defined as the ratio of true positives to the sum of true positives and false negatives, expressed mathematically as:  
\[Recall=\frac{True\ positives}{True\ positives+False\ negatives} \tag{26}\]This formula measures the ability of the model to correctly identify all relevant instances of a positive class. The \(F1\)_score_ is the harmonic mean between precision and recall. This metric provides a balanced assessment of the model's performance, which is particularly useful when precision and recall are in conflict. The \(F1\)_score_ is often chosen to measure the performance of a model faced with an unbalanced dataset, as it incorporates both metrics into one value. The \(F1\)_score_, which is the harmonic mean of _precision_ and _recall_, is expressed as follows:  
\[F1\;score=2\cdot\frac{Precision\;\cdot\;Recall}{Precision+Recall} \tag{27}\]  
This formula combines precision and recall into a single metric, providing a balanced measure of a model's performance, especially in scenarios where false positives and false negatives are of varying importance. The analysis of these three key indicators provides a detailed insight into the ability of our model to provide highly accurate, comprehensive, and relevant answers, which is crucial for its application in the context of educational materials. The hybrid model was assessed using the Skicit-learn Python dataset, i.e., 20 Newsgroups dataset, a well-known, public collection of documents from different newsgroups (Figure 17). This dataset was selected for evaluation purposes due to its representation and coverage of various topics. By utilizing this benchmark dataset, the performance of the model should be thoroughly evaluated in a real-world scenario involving multi-class text classification. This evaluation provides valuable insights into the model's capacity to accurately categorize documents across diverse subjects, as well as its ability to handle complex, multi-topic text data with effectiveness and resilience. When evaluating the performance of the model across different categories, not only the accuracy of the predictions is considered but also their distribution across the various classes. Therefore, an additional layer of precision and reliability is incorporated in a code, as when faced with uncertainty, it transparently responds by indicating that it does not possess the necessary information.