## 2 Materials and Methods
### Machine Learning Methods  
In this study, eight different machine learning models were used to predict biomass gasification gases. The reason for selecting these models is to determine the most successful method for gas composition prediction by comparing their modeling performances in both  
Figure 3: Influence of input parameters on the prediction of CO levels for the original and normalized dataset. linear and nonlinear structures. Additionally, the diversity of the models used allows for examining their capacity to adapt to different data characteristics. Random Forest (RF) Regressor is an ensemble learning model that builds multiple decision trees and combines their results to make more accurate predictions [78]. Each tree is trained on a random subset of the data, and the final prediction is made by aggregating the votes of all trees. This method improves generalization capacity while reducing the risk of overfitting and is particularly effective with large datasets. RF ensures high accuracy for input features and is particularly successful in capturing complex, nonlinear relationships. In this study, the model was implemented using the _RandomForestRegressor_ function in Python. Extreme Gradient Boosting (XGBoost) is an optimized version of the gradient boosting algorithm, offering a high-performance and efficient learning process. XGBoost works by minimizing the error rates of previous trees with each new tree, with each iteration enhancing the model's accuracy. This method is known for fast learning processes, high accuracy, and regularization techniques. XGBoost's success is driven by optimization methods such as regularization and early stopping, which prevent overfitting. Moreover, it operates efficiently with large datasets and complex relationships, minimizing computational time. XGBoost's parallel processing capability makes it especially fast and effective for large datasets. With its high accuracy, speed, and regularization advantages, this model is widely popular for classification and regression problems. The model was implemented using the _XGBRegressor_ function. Light Gradient-Boosting Machine (LightGBM) is an alternative to XGBoost and is specifically designed to work faster and more efficiently with large datasets and high-dimensional features. LightGBM uses a histogram-based learning approach, which leads to faster results with lower memory consumption. This study implemented the model using the _LGBMRegressor_ function in Python. Elastic Net is a regression model that combines the Lasso (L1) and Ridge (L2) regularization methods. While Lasso encourages feature selection, Ridge provides greater regularization (shrinkage). Elastic Net combines the benefits of both methods, improving performance with high-dimensional datasets, and is especially effective in cases of multi-collinearity (highly correlated features). In this study, the model was developed using the _ElasticNet_ function. Adaptive Boosting (AdaBoost) constructs a strong model using a series of weak learners, typically decision trees. Each iteration aims to correct the errors of the previous model to form a more accurate one. AdaBoost takes advantage of combining multiple weak classifiers to achieve high accuracy and strengthens simple models that may tend to overfit. This model was implemented in Python using the _AdaBoostRegressor_ function. Gradient-Boosting Regressor (GBR) implements the basic gradient boosting algorithm, which builds a strong model from weak learners. This algorithm iteratively creates a new model to correct the errors of the previous one, aiming to minimize errors and increase accuracy with each step. GBR typically requires more computational power and time, as each iteration focuses on correcting the errors from the previous model, which increases computational costs. GBR can learn nonlinear and complex relationships, but it can be prone to overfitting without regularization. However, with the right hyperparameter optimization, GBR can provide high accuracy and strong generalization capabilities.