## 3 Results
### Trained Model Performance
Proceeding with the model evaluation, the confusion matrices are displayed below for each method:  
**Random Forest Classifier** (Figure 5)  
**True Positives (TPs)**: 98--Instances of the positive class correctly identified by the model. **True Negatives (TNs)**: 89--Instances of the negative class correctly identified by the model. **False Positives (FPs)**: 0--Instances of the negative class incorrectly identified by the model. **False Negatives (FNs)**: 0--Instances of the positive class incorrectly identified by the model. **Positive Predictive Value (PPV) for Random Forest**: 1.0, meaning that during the prediction of a positive instance, the Random Forest is correct 100% of the time. **Negative Predictive Value (NPV) for Random Forest**: 1.00, meaning that during the prediction of a negative instance, the Random Forest is correct 100% of the time. **Gradient Boosting** (Figure 6)  
**True Positives (TPs)**: 98--Instances of the positive class correctly identified by the model. **True Negatives (TNs)**: 89--Instances of the negative class correctly identified by the model. **False Negatives (FNs)**: 0--Instances of the positive class incorrectly identified by the model. **Positive Predictive Value (PPV) for Gradient Boosting**: 1.0, meaning that during the prediction of a positive instance, the Random Forest is correct 100% of the time. **Negative Predictive Value (NPV) for Gradient Boosting**: 1.00, Meaning that during the prediction of a negative instance, the Random Forest is correct 100% of the time. **Figure 5. Confusion matrix of Random Forest Classifier after training. **  
**Ridge Classifier** (Figure 7)  
**True Positives (TPs)**: 91--Instances of the positive class correctly identified by the model. **True Negatives (TNs)**: 88--Instances of the negative class correctly identified by the model. **False Positives (FPs)**: 1--Instances of the negative class incorrectly identified by the model. **False Negatives (FNs)**: 7--Instances of the positive class incorrectly identified by the model. **Positive Predictive Value (PPV) for Ridge Classifier**: 0.99, meaning that during the prediction of a positive instance, the Random Forest is correct 99% of the time. **Negative Predictive Value (NPV) for Ridge Classifier**: 0.93: Meaning that during the prediction of a negative instance, the Random Forest is correct 93% of the time. Both Random Forest classifier and Gradient Boosting performed exceptionally well, both outperforming Ridge Classifier with no false positives. This resulted in perfect PPV and NPV scores. Confusion matrices indicate that Gradient Boosting and Random Forest perform better than Ridge Classifier, taking precision and overall prediction accuracy into account. The Ridge Classifier has a lower accuracy than the previous two, with PPV and NPV values also lower when compared to the ensemble methods. This concludes that Ridge Classifier struggles with distinguishing between classes and is therefore less reliable. Depicted below are the learning curves of the machine learning model's performance over time. Below we present the results of the learning curves from all three classification models during training. **Random Forest Classifier:**  
**Training score**: Depicts the performance of the model during its training with the training dataset. Random forest classifier shows that the model is fitting very well the training data (Figure 8). **Cross validation score**: This curve depicts the performance of the model during training using a validation or test set unknown to the model and therefore showing how well the model generalizes unseen data. Random forest classifier shows an incremental cross-validation score from the beginning until 200 samples and then follows a slow but steady incremental course, reaching its peak near 600 images. This indicates the high data suitability of the dataset used since the cross-validation score almost meets with the training score (see Figure 8). Figure 7: Confusion matrix of Ridge Classifier after training.