Discussing the article: "Evaluating ONNX models using regression metrics"

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Check out the new article: Evaluating ONNX models using regression metrics.

Regression is a task of predicting a real value from an unlabeled example. The so-called regression metrics are used to assess the accuracy of regression model predictions.

Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc.

The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions. It is important to understand the difference between them. It can be formulated as follows:

  • The loss function arises at the moment when we reduce the problem of building a model to an optimization problem. It is usually required that it has good properties (e.g. differentiability).

  • A metric is an external objective quality criterion, usually depending not on the model parameters, but only on the predicted values.


MQL5 language features the following metrics:

  • Mean Absolute Error, MAE
  • Mean Squared Error, MSE
  • Root Mean Squared Error, RMSE
  • R-squared, R2
  • Mean Absolute Percentage Error, MAPE
  • Mean Squared Percentage Error, MSPE
  • Root Mean Squared Logarithmic Error, RMSLE

Author: MetaQuotes

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