Discussing the article: "Regression models of the Scikit-learn Library and their export to ONNX"
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Check out the new article: Regression models of the Scikit-learn Library and their export to ONNX.
In this article, we will explore the application of regression models from the Scikit-learn package, attempt to convert them into ONNX format, and use the resultant models within MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions for both float and double precision. Furthermore, we will examine the ONNX representation of regression models, aiming to provide a better understanding of their internal structure and operational principles.
Scikit-learn is one of the most popular and widely used libraries for machine learning in the Python community. It offers a wide range of algorithms, a user-friendly interface, and good documentation. The previous article, "Classification Models of the Scikit-learn Library and Their Export to ONNX", covered classification models.
In this article, we will explore the application of regression models in the Scikit-learn package, compute their parameters with double precision for the test dataset, attempt to convert them to the ONNX format for float and double precision, and use the obtained models in programs on MQL5. Additionally, we will compare the accuracy of the original models and their ONNX versions for float and double precision. Furthermore, we will examine the ONNX representation of regression models, which will provide a better understanding of their internal structure and operation.
Author: MetaQuotes