Discussing the article: "Stepwise feature selection in MQL5"
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Check out the new article: Stepwise feature selection in MQL5.
In this article, we introduce a modified version of stepwise feature selection, implemented in MQL5. This approach is based on the techniques outlined in Modern Data Mining Algorithms in C++ and CUDA C by Timothy Masters.
Traditional stepwise feature selection is a technique used to identify an optimal subset of variables from a larger pool of candidate features for a machine learning task. This process begins by evaluating each candidate feature individually to select the most promising variable for inclusion in the final model. Subsequently, additional candidates are tested for their contribution in combination with those already chosen, continuing until a target level of predictive or classification performance is achieved.
In this article, we examine the limitations of conventional stepwise feature selection, such as its potential for overfitting and its challenges in capturing interactions between features. We then introduce an enhanced algorithm designed to address these issues, implemented in MQL5, which provides flexible integration with various supervised learning methods.
This improved approach, was developed by Timothy Masters and described in his book Modern Data Mining Algorithms in C++ and CUDA C. Finally, we showcase the algorithm’s practical application by using it to select optimal variables for a sample regression task, illustrating its effectiveness.
Author: Francis Dube