## 3 Machine Learning Methods
### Bagging and Bossing Approaches  
Ensemble approaches are used to optimize data mining and machine learning operations. Additionally, these strategies assist in mitigating unneeded training data concerns by merging and pooling numerous suboptimal prediction models (i.e., sub-models of components). By carefully changing training data, it is possible to generate a large number of sub-models/classifier components (1, 2,..., M). This could lead to a more productive learner. The optimal predictive model, on the other hand, can be built by averaging combine procedures on verified sub-models. Bagging, along with the bootstrap resampling strategy and benefit collation, is recognized as one of the most prevalent ensemble modeling strategies. The component models are replaced by the first training set (bootstrap tests up to the training set size). Many data points may appear several times in product models, while others may not exist at all. Finally, the outcome is obtained by averaging the component model outputs.  
The boosting technique generates a cumulative model, which results in more accurate component development than an individual model. Meanwhile, boosting positions dependent sub-models in the final model using weighted averages of dependent sub-models. The present work used MLPNN, decision trees, and random forests with gradient boosting, as well as boosting and bagging, to predict the mechanical properties of commonly used concrete.  
Parameter Tuning for Ensemble Learner  
The tuning parameters for the model employed in ensemble procedures can be (i) variables correlated with the ideal number of sample learners and (ii) learning rates and other characteristics that specifically impact ensemble techniques.  
In this study, the optimal range of sub-models was determined, bagging and boosting ensemble models (20 each) with 10, 20, 30,..., 200 component sub-models were constructed for base learners, and correlation with high coefficient values was used to choose the best model. Figure 7 illustrates the relationship between ensemble model accuracy and the number of component sub-models. According to the report, the ensemble model with boosting exhibited a high correlation coefficient with the expected aspect. Additionally, decision trees with bagging and boosting as ensemble models of 20 and 130 sub-models had a substantial relationship, as illustrated in Figure 7a,b. Similarly, ensemble machine learning with 20 and 40 ensemble models demonstrated a significant effect, as illustrated in Figure 7c,d.  
Initially, the research showed that using ensemble modeling improved the accuracy of both models. However, employing 200 or more component sub-models for the bagging ML technique and 10 or more for the boosting algorithms yielded more accurate results. Table 5 describes the architectures used in the ensemble models. It is worth noting that the learning rate for the study was 0.9 when both ML ensemble ML methods, bagging, and boosting algorithms, were used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approaches** & **Ensemble** & **Machine Learning** & **Ensemble Models** & **Optimum** & **R-Value** \\
**Used** & **Techniques** & **Methods** & **Ensemble Models** & **Estimator** & **R-Value** \\ \hline Individual & - & DT & - & - & 0.80 \\  & - & MLPNN & - & - & 0.77 \\ \hline Ensemble & Bagging & Decision tree- bagging & (10, 20, 30... 200) & 20 & 0.87 \\  & & Multilayer perceptron & (10, 20, 30... 200) & 20 & 0.83 \\  & & network- bagging & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed information of the sub-models of ensemble approaches.  
Figure 7: Results of ensemble ML models with their sub-models. (**a**) DT-Bagging; (**b**) DT-Adaboost; (**c**) MLPNN-Bagging; (**d**) MLPNN-Boosting.