Discussing the article: "Ensemble methods to enhance numerical predictions in MQL5"
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Check out the new article: Ensemble methods to enhance numerical predictions in MQL5.
In this article, we present the implementation of several ensemble learning methods in MQL5 and examine their effectiveness across different scenarios.
Machine learning often produces multiple predictive models of varying quality. Practitioners typically evaluate these models and select the best-performing one for real-world applications. However, this article explores an alternative approach: repurposing seemingly inferior models by combining their outputs to potentially enhance overall predictive performance. We will examine various techniques for combining predictions and demonstrate their implementation in pure MQL5. Finally, we will compare these methods and discuss their suitability for different scenarios.
To formalize the concept of combining model predictions, let’s introduce some key notation. Consider a training set consisting of K data points, each represented as a pair (xi,yi), where xi is a predictor vector and yi is the corresponding scalar response variable we aim to predict. Suppose we have N trained models, each capable of making predictions. When presented with a predictor x, model n generates a prediction denoted as f_n(x). Our goal is to construct a consensus function f(x) that effectively combines these N individual predictions, yielding a more accurate overall prediction than any single model.
Author: Francis Dube