Deep learning models have proven to be incredibly powerful in the field of machine learning. However, one of their limitations is the requirement for large amounts of data to train effectively. Additionally, the complexity of these models makes it challenging to interpret their understanding of the data. In such cases, linear models can be a valuable alternative. Linear regression and logistic regression are linear models that can help overcome these challenges, especially when dealing with linearly separable data.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/sklearn-linear-models-doing-more-with-less-data