# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 6 Discussion
### Comparison between Other Methods and WorkloadGPT (Q1)  
This study established comparison models using random forest, support vector machine (SVM), K-nearest neighbors (KNNs), gradient boosting decision tree (GBDT), and a few-shot logic applied with ChatGPT-4 to evaluate their performance against WorkloadGPT.  
**Random Forest**: Random forest is an ensemble learning method that improves the accuracy and stability of a model by constructing multiple decision trees and combining their predictions. The model's structure consists of several independently trained decision trees, and the final classification result is determined by majority voting. In this model, the number of decision trees (n_estimators) was set to 150, the maximum number of features considered at each split (max_features) was set to 3, and the maximum depth of the decision trees was left unrestricted (i.e., defaulting to none), as is typical.  
**Support Vector Machine:** Support vector machine (SVM) is a widely used supervised learning model for classification problems. It works by finding the optimal hyperplane that maximizes the margin between different classes. In this study, a linear kernel function (kernel = 'linear') was used to create the SVM model, which was then trained on the training set. The regularization parameter C was set to the default value of 1 to balance training error and generalization ability.  
**KNNs:** K-nearest neighbors (KNNs) is a supervised learning model used for classification problems. It works by calculating the distance between the input sample and the training samples, then selecting the K nearest neighbors to vote on the classification. In this model, the features were first standardized and the KNNs model was then trained. The number of neighbors (K) was set to 5, and Euclidean distance was used as the distance metric to optimize classification performance.  
**GBDT:** Gradient boosting decision tree (GBDT) is a powerful ensemble learning method that improves model accuracy and stability by constructing a series of decision trees and iteratively optimizing the prediction errors of each tree. After standardizing the features, the GBDT model was trained. In this model, the learning rate was set to 0.004, the maximum depth of the decision trees was set to 6, the subsample ratio and the ratio of random sampling for each tree were both set to 0.8, and the number of decision trees was set to 120. These settings achieved a good balance between model complexity and training time.  
All models were tested on the same validation set, using data from pilot IDs 17-20. The classification accuracy for each model is detailed in Figure 9.  
In terms of accuracy, only WorkloadGPT exceeded 80%, outperforming the second most accurate model, random forest, by 18.1%. This indicates WorkloadGPT's significant advantage in accuracy compared with the other algorithms. Specifically, compared was ChatGPT-4, which utilizes few-shot learning methods, WorkloadGPT improved the model accuracy by 12.3%, with a runtime of only 1.76 s, demonstrating rapid response and strong real-time performance. This comprehensive performance enhancement was probably a reflection of ChatGPT-4's lack of pre-training knowledge in the specific domain of pilot workload. Therefore, training a large language model specifically for pilot workload recognition is essential.