# Application of Machine Learning Models for Baseball Outcome Prediction
## 2 Method
### Machine Learning Methods  
In this study, Python3.13 was employed for data preprocessing, model training, performance evaluation, and model interpretability. A five-fold cross-validation strategy was adopted to develop and validate five machine learning models: decision tree, logistic regression, Artificial Neural Network, Random Forest, LightGBM, and XGBoost. Decision Tree (DT). Decision trees are supervised learning models that split data into branches based on feature values, forming a tree-like structure. Each internal node represents a decision based on a feature, while each leaf node corresponds to a predicted outcome. This method is valued for its interpretability and simplicity, though it may overfit the data if not pruned properly. In this study, decision trees are used as a baseline due to their transparency and effectiveness in identifying influential variables, which has been demonstrated in materials classification tasks. Logistic Regression (LR). Logistic regression is a fundamental classification technique used to model the probability of a binary outcome. It employs the logistic function to estimate the likelihood that an input belongs to a certain class, based on a linear combination of features. Despite its simplicity, it remains robust and interpretable, making it a reliable baseline for many classification problems, including those in materials science. Artificial Neural Network (ANN). Artificial Neural Networks (ANNs) are computational models composed of interconnected layers of artificial neurons that process inputs through nonlinear transformations. They are particularly effective in capturing complex and nonlinear relationships in data, especially when sufficient training data are available. ANNs have demonstrated strong predictive performance in various machine learning applications, including those in materials informatics and other domains. In this study, we employed a feedforward neural network architecture for binary classification. The model consists of three layers: an input layer, one hidden layer, and an output layer. The hidden layer comprises 64 neurons with rectified linear unit (ReLU) activation functions, followed by a dropout layer with a dropout rate of 0.3 to mitigate overfitting. The model is trained using the cross-entropy loss function, which is appropriate for binary classification tasks. Random Forest (RF). Random Forest is an ensemble learning method that builds a collection of decision trees on bootstrapped samples of the data and averages their predictions. It introduces additional randomness by selecting a subset of features at each split, which enhances generalization and reduces overfitting. Random Forests offer a balance between accuracy and interpretability and have shown strong performance in materials-related predictive modeling. Extreme Gradient Boosting (XGBoost). XGBoost is a high-performance implementation of gradient boosting that builds decision trees sequentially, where each tree attempts to correct the errors of the previous ones. It incorporates regularization to control overfitting and leverages efficient computation strategies such as parallel processing and tree pruning. XGBoost is well-suited for structured data and has become a standard in high-stakes machine learning applications. Light Gradient Boosting Machine (LightGBM)  
LightGBM is a gradient boosting framework that uses tree-based learning algorithms optimized for speed and memory efficiency. Unlike traditional boosting methods, it employs histogram-based algorithms and leaf-wise tree growth, which significantly acceleratetraining while maintaining accuracy. LightGBM is particularly suitable for large-scale structured data and supports parallel and GPU learning, making it an effective choice for performance-critical applications. To enhance model performance and mitigate overfitting, we conducted hyperparameter optimization using grid search in conjunction with five-fold cross-validation.