## 4 Proposed Framework
In this instance, \(P(y|X)\) was the probability of the class \(y\) (for MIR or NMIR), according to the incident description (\(X\)). The maximum probability function provided the classification label, either MIR or NMIR. * **GB**[43]: Gradient boosting classifier is an approach developed to train a weak hypothesis iteratively to arrive at a better hypothesis. Gradient boosting combines the previous model with the next generated model to minimize the prediction error. It minimizes the loss function iteratively, starting with a negative gradient, i.e., a weak hypothesis. In this work, we performed a binary classification (MIR or NMIR) using the description column of the incident. The algorithm began with one leaf node that predicted the initial value for each description of the incident. Next, the algorithm used the \(log(odds)\) of the target value, yielding an average survival value assigned to our initial leaf node. If the probability of surviving exceeded 0.5, we first classified every sample in the training dataset as MIR. (Note: where 0.5 was a common threshold value associated with binary classification decision based on probability). * **XGBoost**[44]: Extreme gradient boosting is an optimized GB technique that provides efficient, flexible, and portable tree model results. It offers parallel tree boosting that provides solutions quickly and accurately. XGBoost is efficient for open-source implementation and significantly distributed environments, such as Hadoop, Sun Grid Engine (SGE), and Message Passing Interface (MPI). It predicts the residual or error of prior models to obtain its final predictions. The gradient descent algorithm minimizes loss when adding new models. We trained the model with 500 trees and a  
Figure 4: Graphical representation of the pipeline stages for training and testing for ML, DL, and transformer models. depth (_max_depth_) of 1 (for the root node). As compared to other available boosting techniques, e.g., GB, XGBoost was fast, memory efficient, and highly accurate [45]. * **Catboost**[46]: Category and boosting adopts minimal variance sampling, a type of stochastic gradient boosting with weighted sampling. In this instance, the weighted sampling occurred at the tree level and not the split level. It grows as a balanced tree; the split score minimizes the loss and maximizes the accuracy score. Changing policy parameters (with a penalty function at level nodes) is also possible. In contrast to a traditional approach, this study addressed the MIR and NMIR classification problems. We trained the model with 400 trees and a depth (_max_depth_) of 1 (root node only). As compared to the XGBoost, Catboost was twice as fast with better accuracy [47]. * **SVM**[48]: Support vector machines utilize associated learning to analyze data for classification. Based on the descriptions of known incidents, SVM built a model that assigned MIR or NMIR labels to new incidents. The algorithm considered all incident descriptions and mapped them to a space that maximized the distance between the two classes. In the context of this study, we had a training dataset of incident descriptions labeled \"0\" for MIR and \"1\" for NMIR, \((x_{1},y_{1})\ldots(x_{n},y_{n})\) where \(y_{i}\) is either 0 or 1, each denoting to a point \(x_{i}\), where each \(x_{i}\) is a \(p\)-dimensional real vector. We were interested in the maximum-margin hyperplane that divided the group of points \(x_{i}\), for which \(y_{i}=0\) from the group of points for which \(y_{i}=1\), which was defined so that the distance between the hyperplane and the nearest point \(x_{i}\) from either group was maximized. Any hyperplane could be written as the set of points \(x\), satisfying \(w^{T}\mathbf{x}-b=0\), where \(w\) was the (not necessarily normalized) normal vector to the hyperplane. This was similar to [PERSON]'s normal form, except that \(w\) was not necessarily a unit vector. The parameter \(\frac{b}{\|\mathbf{w}\|}\) determined the offset of the hyperplane from the origin along the normal vector \(W\).