# Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
## 3 Research Methodology
### Classification Algorithm Performance Assessment  
Before the DT classification process is deployed, a classification algorithm performance assessment is essential to be conducted to define the optimal algorithm for such a classification. To determine which algorithms might perform better for the current dataset, there are several factors that need to be considered, such as the nature of the data, its features, and the specific problem context (classification, regression, etc.). Without applying each algorithm to the data, it is difficult to predict with certainty which one will underperform. Decision trees offer distinct advantages over logistic regression, KNN (K-nearest neighbors), and naive Bayes in various situations, primarily due to their inherent flexibility and robustness to certain types of data. They excel at handling complex and non-linear relationships between features and the target variable, making them particularly useful for datasets where linear models like logistic regression might struggle to capture the underlying patterns. Decision trees naturally accommodate both categorical and numerical data, and their hierarchical structure allows for an intuitive interpretation and analysis, akin to human decision-making processes. This interpretability is a significant advantage, particularly in fields where understanding the decision rationale is as important as the decision itself [21; 22; 23; 24; 25].  
Moreover, decision trees are less sensitive to outliers and missing values compared to logistic regression and naive Bayes, which can be significantly impacted by such anomalies due to their reliance on assumptions about the data distribution. While KNN is non-parametric and also robust to the non-linearity of data, it can suffer from the curse of dimensionality and become computationally intensive with large datasets, a drawback that decision trees mitigate through feature selection and dimensionality reduction at each split. However, decision trees can be prone to overfitting, especially with very complex trees, and may require techniques like pruning or ensemble methods like random forests to maintain generalizability. In contrast, logistic regression offers a more straightforward, less computationally intensive option for binary outcomes with linear relationships, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Value** \\ \hline Unique bins/classes & 5 \\ Most frequent bin & \(-199.8\)–\(1.0\) (35,536 occurrences) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Binned fish mortality classes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Value** \\ \hline Count & 37,203 \\ Mean & \(-44.34\) \\ Standard deviation & 83.75 \\ Minimum & \(-995.00\) \\ Maximum & \(-1.00\) \\ Missing values & Non-missing values \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fish mortality statistic values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class No** & **Range** & **Instances** \\ \hline
1 & From \(-995\) to \(-796\) & 74 \\
2 & From \(-796\) to \(-597\) & 139 \\
3 & Form \(-597\) to \(-398\) & 301 \\
4 & From \(-398\) to \(-199\) & 1153 \\
5 & From \(-199\) to \(-1.0\) & 35,536 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Equal width binning strategy for the “Deaths” variable.  
naive Bayes can be particularly effective in probabilistic classification tasks, such as spam detection, despite its simplicity and strong independence assumptions [21; 22; 23; 24; 25].