# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### Gaussian Process Regression (GPR)
Decision Tree (DT)  
Decision tree regression predicts dataset output after developing a decision tree structure. As a supervised learning method, this model analyses the data to create a model and forecast the output based on the input values [45]. A DT is a data structure that can visualize and analyze data. It splits the data according to specified criteria and assigns each choice a probability. The data are first partitioned into nodes or points, and then branches are drawn between them to represent the decisions that need to be made. This process, known as \"splitting,\" is determined by the available data results. Each decision tree branch represents a possible outcome and is assigned a probability based on the values of the other branches. The model is evaluated using new data, and its accuracy and reliability are assessed based on the results [46]. Decision tree regression equations are often complex algorithms, such as randomized decision trees, gradient-boosted trees, and random forests. These equations use a variety of mathematical processes to build a model and make predictions. Decision tree regression is a valuable tool for non-technical people because it is easy to use, does not require much computing power, and can be very accurate. However, it may be less precise and reliable than other models, and it can take a very long time to train. The model may also struggle with overly complex datasets or situations where the data must be balanced. Finally, the trees may grow to be too big and unreadable [47]. A fully connected neural network (FCN) is a type of artificial neural network (ANN) that employs direct connections between nodes in various layers. It is a popular deep learning framework. In this architecture, all nodes from one layer are linked to those from the next. FCN layers are commonly used in supervised (classification, regression) and unsupervised (density estimation) learning tasks. By analyzing its structure and composition, FCNs can accurately predict a material's chemical, mineral, and physical characteristics. They work by developing mathematical models that are meant to describe the relationship between the inputs (features) and the outputs (results) (mineral content). The network of neurons compresses and processes information from the input to generate an output. Each neuron is connected to every other neuron with weights, and a learning process called backpropagation is used to update the weights [48]. FCNs can be used to predict mineral composition using a simple equation and the backpropagation algorithm. Backpropagation is a method for updating the weights of each neuron based on the degree of discrepancy between the input and the desired output. This helps the neural network minimize error through data-driven learning. Backpropagation is used to change the weights between randomly allocated neurons. FCNs are well-suited for estimating mineral content because of their accuracy, adaptability to different input/output formats, and ability to capture non-linear correlations. However, they also have drawbacks, including overfitting and poor interpretability. Over-fitting occurs when a neural network learns the training data too well, leading to poor performance on new data. Additionally, FCNs can be difficult to interpret, making it challenging to understand why a particular prediction was made.