Discussing the article: "Ensemble methods to enhance classification tasks in MQL5"
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Check out the new article: Ensemble methods to enhance classification tasks in MQL5.
In this article, we present the implementation of several ensemble classifiers in MQL5 and discuss their efficacy in varying situations.
The classification ensembles discussed in this article operate under specific assumptions about their component models. First, it is assumed that these models are trained on data with mutually exclusive and exhaustive class targets, ensuring that each instance belongs to exactly one class. When a "none of the above" option is required, it should either be treated as a separate class or managed using a numerical combination method with a defined membership threshold. Furthermore, when given an input vector of predictors, component models are expected to produce N outputs, where N represents the number of classes. These outputs may be probabilities or confidence scores that indicate the likelihood of membership for each of the classes. They could also be binary decisions, where one output is 1.0 (true) and the others are 0.0 (false), or the model outputs could be integer rankings from 1 to N, reflecting the relative likelihood of class membership.
Some of the ensemble methods we will look at benefit greatly from component classifiers that produce ranked outputs. Models capable of accurately estimating class membership probabilities are usually highly valuable, it's just that there are significant risks to treating outputs as probabilities when they are not. When there is doubt about what model outputs represent, converting them to ranks provides may be beneficial. The utility of rank information increases with the number of classes. For binary classification, ranks offer no additional insight, and their value for three-class problems remains modest. However, in scenarios involving numerous classes, the ability to interpret a model's runner-up choices becomes highly beneficial, particularly when individual predictions are fraught with uncertainty. For example, support vector machines (SVMs) could be enhanced to produce not only binary classifications but also decision boundary distances for each class, thereby offering greater insight into prediction confidence.
Author: Francis Dube