Mindful Modeler • 319 implied HN points • 03 Oct 23
- Machine learning excels because it's not interpretable, not in spite of it.
- Embracing complexity in models like neural networks can effectively capture the intricacies of real-world tasks that lack simple rules or semantics.
- Interpretable models can outperform complex ones with smaller datasets and ease of debugging, but being open to complex models can lead to better performance.