# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 2 Materials and Methods
### Proposed Model  
#### 2.7.1 Stacking Ensemble Learning (SEL)  
Ensemble learning is a very powerful machine learning technique [91; 92; 93] that uses the combined output of two or more models or weak learners to address a specific computational intelligence issue. The main goal of ensemble learning is to boost model performance in areas like classification and prediction. A machine learning model called an ensemble model integrates the forecasts from two or more models.  
Stacking is an ensemble learning technique that aggregates several machine learning algorithms through meta-learning. The algorithms at the base level are trained using a comprehensive training dataset; afterwards, a meta model is trained on the final results of the total base level model as features. Bagging, boosting, and stacking are the three most used ensemble learning techniques in machine learning. Significant differences between comparable models can be averaged using bagging to reduce discrepancies. Boosting constructs numerous sequential models to reduce the bias, while minimizing variation. Stacking is an augmentation of the voting classifier or voting regressor by an upper level (amalgamation level) that learns from the superior combination of the distinctive outputs. A different classifier or regressor is located at the top of the stack. Stacking is exceptionally beneficial when the outputs of different algorithms are likely not to be the same, and this is practically always the situation with regression. Stacking algorithms have the capacity to extend over a number of layers. This makes them very strenuous to train. The stacking approach is distinct. Stacking is used to investigate the space of many models for the same problem. According to the concept, it is possible to approach a learning problem using several kinds of models that can only learn a portion of the problem space. It will build multiple distinct learners and use them to create a transitional prediction, one for each learned model. Subsequently, a new model that gains knowledge from earlier predictions for a similar goal is added.  
The ultimate model (meta model) is referred to as being stacked on top of all the others, hence the appellation. As a result, the general performance is enhanced. The stacking technique usually produces a model that performs better than any of the individual transitional models.  
To forecast monkeypox transmission rate, this study used stacked ensemble learning (SEL) models. Stacking is a well-known ensemble modeling approach in machine learning. It entails merging several weak learners in parallel such that, by combining them with meta learners, we may anticipate better future predictions [94]. This ensemble strategy works by combining the predictions of numerous weak learners and meta learners to produce a superior output prediction model. Stacking is a technique in which an algorithm takes the outputs of sub-models as input and attempts to learn how to optimally combine the input predictions to get a superior output prediction. The model is called \"stacking\" since it is placed on top of the others.