Discussing the article: "Resampling techniques for prediction and classification assessment in MQL5"
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Check out the new article: Resampling techniques for prediction and classification assessment in MQL5.
In this article, we will explore and implement, methods for assessing model quality that utilize a single dataset as both training and validation sets.
The performance of machine learning models is typically assessed through two distinct phases: training on one dataset and testing with another. However, in situations where collecting multiple datasets may be impractical due to resource constraints or logistical limitations, alternative approaches have to be employed.
One such method involves using resampling techniques to evaluate the performance of prediction or classification models. This approach has been shown to provide reliable results despite its potential drawbacks. In this article, we will explore a novel methodology for assessing model quality that utilizes a single dataset as both training and validation sets. The primary reason for applying these methods is the limited availability of data for testing purposes.
As such, practitioners must employ sophisticated resampling algorithms to produce performance metrics comparable to those generated by more straightforward approaches. These techniques require significant computational resources and may introduce complexity into model development processes. Despite this trade-off, employing resampling-based assessment strategies can be valuable in certain contexts where the benefits outweigh the costs.
Author: Francis Dube