# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Evaluation Metrics Utilized to Measure Performance  
An evaluation of machine learning models is essential for ensuring their functionality, robustness, and comparative effectiveness against other solutions. It highlights a model's performance clearly, addressing its strengths and weaknesses. However, this task is challenging due to the complex decision-making processes of models, variability in training data, and differing objectives across projects. Consequently, to overcome these hurdles and gain a comprehensive understanding of a model's capabilities, employing various evaluation metrics is crucial. Therefore, multiple metrics are employed for a nuanced assessment of a model's performance, catering to the specific demands of each unique application ([PERSON] et al., 2020). Accuracy is the most utilized performance metric, adopted in twenty-three publications, as shown in Table 3. The metric signifies the proportion of correct predictions relative to the total sample, serving as a fundamental gauge of model performance. A notable adaptation for multi-class scenarios is Top-N accuracy. This variation considers the N most probable classes predicted by the model for each instance; and if the actual class is among these top N predictions, the prediction is deemed correct. Top-N accuracy offers a more flexible yet insightful assessment of performance, and such a measure broadens the evaluation scope, accommodating the complexities of multi-class classifications ([PERSON] et al., 2020). On a practical level, this measure proves valuable when the most likely bee species are already suggested through other means, such as expert knowledge, specific geographic contexts, or additional technological methods. Precision, recall (sensitivity or true positive rate), and specificity were used in nine, eight, and one publications, respectively, and offer detailed views of a model's performance beyond what accuracy alone can provide. [PERSON] et al. (2020) evaluated models with these metrics and found that although ResNet50 demonstrated a slightly higher accuracy (0.9468) compared to VGG's accuracy (0.9449), VGG exhibited a superior precision of 0.9448 as compared to ResNet50's precision of 0.9426. This makes VGG a better choice when the accurate identification of the positive class is critical, despite its marginally lower overall accuracy compared to ResNet50. Indeed, precision is commonly used when the consequences of falsely identifying negatives as positives (false positives) are severe. For instance, identifying invasive bee species accurately is crucial to prevent them from being mistakenly classified as harmless native species, which could lead to inappropriate management decisions, and in such case, high precision ensures that measures to control or eradicate invasive species are only applied to the correct targets, thereby protecting the native bee populations and maintaining ecological balance. On the other hand, recall is crucial when it is important to identify every possible bee of a specific bee species, particularly in situations where missing the classification of a single bee carries significant consequences. For instance, recall becomes important when identifying species known to produce toxic honey. Ensuring that no such species are missed in the identification process is crucial to prevent the collection and consumption of dangerous honey, protecting public health and maintaining the safety of bee products in the market. High recall in this context means effectively catching every instance of species that could pose risks, minimizing the chance of overlooking any hazardous bees. Finally, specificity measures the ability of the model to correctly identify negatives, meaning it accurately identifies those instances that do not belong to the positive class. Specificity would be important when distinguishing non-poisonous bee species from poisonous ones.