Machine Learning for Industrial Applications

7Model Ensembles

Machine learning algorithms have limitations, making it difficult to create a high-accuracy model [1]. A single algorithm may not be able to deliver the best forecast for a given dataset [2]. Ensemble modeling is a method of predicting a result by combining several different base models. The goal of employing ensemble models is to lower the prediction’s generalization error. When using the ensemble approach, the prediction error lowers as long as the basis models are diverse and independent [3]. It is a solution to overcome the technical challenges like high variance, low accuracy, and features of noise and bias in building a single estimator [2]. Ensemble approaches in machine learning have risen in prominence as a result [4]. The same is graphically presented in Figure 7.1.

The main goal of ensemble learning is to enhance the performance of models in areas like classification, prediction, and function approximation. We can summarize ensemble learning in the following straightforward manner:

A machine learning model called an ensembled model integrates the forecasts from two or more models.

The most popular ensemble methods are as follows [4]:

• Bagging
• Boosting
• Stacking

Each strategy is described by an algorithm, but their success has generated a slew of extensions and related ideas. As a result, it is better to think of them as a set of methodologies or conventional approaches to ensemble learning [5].

Figure 2 represents ensemble types.

Figure 7.1 Ensemble ...