# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Classification
#### 4.2.2 MLSpecialReduct
Typically, models using a reduced attribute set will perform similarly or better in terms of accuracy, precision, recall, and F1-score, while being more efficient and easier to interpret. Table 9 below compares various machine learning models after applying the MLSpecialReduce algorithm. Key performance metrics such as precision, recall, and F1-score are evaluated for each model. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** \\ \hline Gaussian Process & 0.85 & 0.85 & 0.85 \\ \hline Random Forest & 0.99 & 0.99 & 0.98 \\ \hline Nearest Neighbors & 0.79 & 0.79 & 0.79 \\ \hline Linear SVM & 0.85 & 0.85 & 0.85 \\ \hline RBF SVM & 0.79 & 0.79 & 0.79 \\ \hline Neural Network & 0.79 & 0.79 & 0.79 \\ \hline Naive Bayes & 0.86 & 0.85 & 0.85 \\ \hline Decision Tree & 0.75 & 0.74 & 0.75 \\ \hline AdaBoost & 0.87 & 0.87 & 0.87 \\ \hline QDA & 0.84 & 0.83 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of classifier models with MLSpecialReduct feature selection. We observe that the **Random Forest** algorithm (Figure 5) achieves the highest performance across all metrics when paired with the MLSpecialReduct feature selection method. This suggests that applying MLSpecialReduct not only enhances the model's computational efficiency but also maintains or improves its predictive capabilities. By focusing on the most critical attributes, Random Forest outperforms other models in terms of precision, recall, and F1-score. #### 5.3.4 Evaluation with MLVarianceThreshold

We benchmarked model performance using **MLVarianceThreshold**, our adapted version of the traditional VarianceThreshold method, to compare against RST-based approaches. MLVarianceThreshold removes low-variance features--attributes with minimal variability and limited predictive value--enhancing efficiency and simplifying models. Benefits include the following:

* **Noise Reduction:** Low-variance features often add noise rather than signal; removing them improves dataset quality and model stability. * **Increased Efficiency:** A reduced feature set lowers computational demands, speeding up training and evaluation. * **Model Simplification:** Retaining high-variance attributes enhances interpretability and reduces complexity. * **Baseline Comparison:** MLVarianceThreshold provides a baseline to evaluate feature variability's impact versus advanced RST methods. Table 10 shows the classifier performance post-MLVarianceThreshold, using precision, recall, and F1-score. MLVarianceThreshold yields modest performance (F1-scores: 0.72-0.77), consistent with its range in Section 5.1 (0.72-0.77), but lags behind RST methods like MLSpecialReduct (F1: 0.99). This underscores the limitations of variance-based selection compared to dependency-driven approaches. Figure 5: Evaluation of Random Forest model with MLSpecialReduct method. #### 5.3.5 Evaluation with MLFuzzyRoughSet

The **MLFuzzyRoughSet** method profoundly impacts model evaluation by pinpointing crucial features. This advanced method prioritizes attributes that decisively influence decision-making processes, ensuring that only the most pertinent data points are retained. By streamlining the attribute selection process, MLFRS not only enhances model performance but also improves interpretability and computational efficiency. Benefits include the following:

* **Optimal Feature Selection:** MLFuzzyRoughSet selects influential attributes, preserving essential information. * **Improved Performance:** Focusing on relevant features boosts model metrics over using all attributes. * **Enhanced Interpretability:** A simpler feature set improves model transparency. * **Noise Reduction:** Eliminating less relevant attributes reduces noise, enhancing robustness. Below is Table 11 comparing various machine learning models after applying the MLFuzzyRoughSet method. Key performance metrics such as precision, recall, and F1-score are evaluated for each model.