# Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
## 3 Research Methodology
### Decision Trees (DTs)  
A variety of data mining classifiers were employed in a comparative analysis to determine the most effective method for predicting fish mortality, focusing on achieving a high classification accuracy. Data mining serves as an approach to extract valuable insights through the utilization of data analysis tools and data mining models. These tools enable the discovery of significant correlations, providing essential information for decision-making and predictive processes. Among the commonly used techniques in data mining, DTs play a pivotal role. The application of data mining aims to unearth new knowledge, establish connections and correlations, and reveal intricate patterns [26; 27]. Efforts to optimize the binning strategy for a maximum classification accuracy involve patterns for understanding the identification and potential correlation between the \"Deaths\" variable and other factors. Among the optimal classification strategies is the ability to explore a range of binned variables and measure their effect on the overall data classification performance using cross-validation methods. In the current experiment, the equal width binning model was used for the \"Deaths\" variable. The initial dataset was divided into training (70%), validation (20%), and testing datasets (10%) [28; 29]. DTs that were trained on each binned target in the training set were evaluated using the validation set and tested using the test set. DTs, a frequently employed classification model in data mining, act as a classification mechanism, interpreting data and assigning values to classes in an \"if-then-else\" sequence. The nodes in the decision tree refer to dataset features, while the branches represent the features values. The top node refers to a superclass which includes the leaf nodes representing the sub-classes. The process involves splitting the entire set of examples into training, validation, and testing datasets [29; 30]. Following this, the DT training process takes place with the training set of examples, where a hypothesis is generated, and the percentage of correctly classified examples in the validation sets is calculated. This procedure is repeated with a diverse range of training data. Finally, the testing process validates the results using new data, with overfitting being a potential challenge to the classification process due to insufficient data [29; 30]. Tree pruning techniques are employed to address overfitting, maintaining the model's simplicity and interpretability. The success rate of the data mining methods was gauged by calculating the average success rate across ten experiments (folds). The model underwent training and assessments using a 10-fold cross-validation approach. A pruned DT classifier with a maximum depth of 3 was applied to eliminate overfitting. The 10-fold cross-validation provided a concise measurement of the model's performance, succeeding in achieving a high average accuracy (95.43%). In the context of decision trees, the Shannon function plays a crucial role in the DT algorithm, serving to assess the utility of each attribute by quantifying the information it contributes to bits. The Shannon function gauges the overall relevance of an attribute, considering its consistency with the existing knowledge. When an attribute conveys a substantial size of information, the Shannon function yields a lower value. It calculates the information gained from the entire set of examples separately calculated for a specific attribute [29].