Discussing the article: "Gaussian Processes in Machine Learning (Part 2): Implementing and Testing a Classification Model in MQL5"

[MetaQuotes]

Check out the new article: Gaussian Processes in Machine Learning (Part 2): Implementing and Testing a Classification Model in MQL5.

In this section, we will look at the implementation of the key interfaces of the library of Gaussian processes in MQL5: IKernel, ILikelihood, and IInference. We will also demonstrate its operation on synthetic data and implement indicators for classification and regression, demonstrating its operation in online mode - with retraining of the model on each new bar.

In the previous article, we learned about the theoretical foundations of the Bayesian machine learning model — Gaussian Processes — and began creating a GP library in MQL5, describing two key classes: GaussianProcess and GPOptimizationObjective.

Here we will complete the library by taking a detailed look at the implementation of the key interfaces: IKernel, ILikelihood, and IInference. After this, we will test the library on synthetic data and write indicators for classification and regression, demonstrating its operation in online mode — with retraining the model on each new bar.

Author: Evgeniy Chernish