In our experiments, we found that increasing the number of data augmentations significantly reduces prediction error, highlighting the importance of robust data augmentation techniques in regression tasks.
The use of anchor data augmentation has shown potential in improving the performance of machine learning models, particularly in preserving the nonlinear structure of data.
Our findings indicate that while Ridge regression benefits from anchor augmentation, the performance varies across different models, underscoring the need for tailored approaches in data augmentation.
Experimentation with both synthetic linear data and real-world datasets, such as California housing data, reveals insights into the applicability of different regression techniques in various contexts.
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