## 4 A Coal Rock Strength Prediction Model Based on the AdaBoost Ensemble Method
### Model Training  
A total of 460 samples were collected from on-site tests, which included measurements of pressure, torque, vibration, and rotational speed. Of these, 368 samples (80%) were randomly selected to form the training set, while the remaining 92 samples (20%) were designated as the testing set.  
The common base learners in the AdaBoost algorithm are as follows: 1 Decision tree base learners: the most frequently used weak learners, which rely on the judgment of a single feature. 2 Naive Bayes base learner: a straightforward classifier based on probability, suitable for tasks such as text classification. 3 K-nearest neighbor (KNN) learning algorithm: it classifies data based on the distance between data points, although it requires significant computational resources. 4 Support vector machine (SVM)-based learners: robust classifiers that can also be employed to create weak learners. 5 Linear regression base learner: appropriate for regression problems and binary classification tasks. To better understand the performance of base learners, the performances of different base learners under different dataset sizes and feature combinations are analyzed in detail. Through experiments, it is found that the SVM-based learning machine has better adaptability in dealing with complex nonlinear relationships, the decision tree-based learning machine is more intuitive in the division of data, and the linear regression-based learning machine has certain advantages in dealing with linear relationships, but its adaptability is relatively weak for complex coal and rock mass strength prediction scenarios. In this paper, we predict the uniaxial compressive strength based on the characteristics of LWD signals using decision tree-based learning, SVM-based learning, and linear regression-based learning within the AdaBoost integrated model.  
Machine learning models contain many hyperparameters, and different combinations of hyperparameters have a great impact on the operation efficiency and generalization performance of the model. Therefore, to obtain a model with good operation efficiency and generalization performance, it is necessary to optimize the hyperparameters. Grid search is a commonly used hyperparameter optimization method. It determines a set of optimal values as the final hyperparameter group of the model by traversing all points on the grid in the hyperparameter space within a given hyperparameter range. In this paper, the grid search method is used to optimize the hyperparameters of the model. The learning rate is used to control the weight update speed of weak classifiers in each iteration. The smaller the learning rate, the slower the learning speed of the model, but it may be more stable. The higher the learning rate, the faster the model's learning speed, but it may be easier to overfit. Several estimators are used to control the number of weak classifiers in the AdaBoost model. The more the number, the higher the complexity of the model, but the higher the computational cost.  
Cross-validation evaluates the performance of each combination by adjusting the combination of different learning rates (from 0.01 to 1.0) and the number of estimators (from 10 to 100). The cross-validation method is used to test the performance of different parameter combinations on the validation set. Finally, the combination with the learning rate of 0.1 and the number of estimators of 50 is selected, which effectively avoids the overfitting problem of the model, improves the generalization ability of the model, and makes the model achieve the best prediction effect.