## 5 Experiment Setup and Metrics
### Evaluation Metrics  
In assessing the models' performance, an array of metrics was employed to precisely measure the effectiveness and accuracy of the algorithms. These metrics encompass fundamental measures including accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC) derived from the Receiver Operating Characteristic (ROC) curve.  
1. **Accuracy**: is a fundamental metric that measures the ratio of correctly predicted instances to the total number of instances in the dataset. A high accuracy score indicates the model's effectiveness in making correct predictions across all classes. It is computed using Equation (1): \[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\] (1)
2. **Area Under the ROC Curve (AUC):** The AUC metric is a valuable tool for assessing a model's ability to distinguish between classes. It is calculated by plotting the Receiver Operating Characteristic (ROC) curve and computing the area under this curve. A higher AUC value (ranging from 0 to 1) signifies better discrimination power of the model;
3. **Confusion Matrix**: offers a comprehensive tabular layout displaying True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) values. It aids in visualizing the model's performance across different classes, identifying misclassifications, and understanding the model's behaviour. These metrics collectively provide a thorough understanding of a classification model's performance, allowing for informed decisions and enhancements in model training and optimization strategies; * **True Positive (TP):** This represents the number of instances where the model correctly predicted the positive class (or the condition being tested) as positive. In medical terms, this could be the number of correctly identified patients with a disease; * **True Negative (TN)**: This represents the number of instances where the model correctly predicted the negative class (or the absence of the condition being tested) as negative. In medical scenarios, this could indicate the number of correctly identified healthy individuals who do not have the disease; * **False Positive (FP):** This indicates the number of instances where the model incorrectly predicted the positive class when it was actually negative. In medical scenarios, it could signify the number of healthy individuals incorrectly identified as having the disease; * **False Negative (FN):** This refers to the number of instances where the model incorrectly predicted the negative class when it was actually positive. In medical contexts, this might represent the number of individuals with the disease incorrectly identified as healthy by the model.
4. **F1 Score**: The F1 score represents the harmonic mean of precision and recall. The F1 score provides a balanced evaluation of a model's precision and recall, making it a robust metric for binary classification tasks. It is computed using Equation (2): \[F1\ Score=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\] (2)
5. **Precision**: It measures the ratio of correctly predicted positive instances to the total instances predicted as positive. A high precision score indicates the model's ability tominimize false positives, ensuring that most of the predicted positive instances are relevant. It is computed using Equation (3):  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
**Recall**: It measures the ratio of correctly predicted positive instances to the actual total positive instances in the dataset. A high recall score indicates the model's effectiveness in capturing most of the relevant instances while minimizing false negatives. It is computed using Equation (4).  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]