Figure 12
From: Sea level variability and modeling in the Gulf of Guinea using supervised machine learning
The architecture of the Gradient Boosting model. It consists of multiple Base Learners (F1, F2, F3) and Additive Outputs (H1, H2, H3), which collectively contribute to the final prediction (Y). The input data (X) represents the input features, and the number of features can be determined by the dimensionality of the input data. The number of Base Learners is determined by the number of estimators specified when creating the model. The 'w1', 'w2', and 'w3', represent the contribution weights of each Base Learner to the final prediction. The final prediction (Y) is the output of the Gradient Boosting model, which is a combination of the additive outputs from all Base Learners.
