alt Hacker NewsDo neural networks work better than other models? They can definitely model a wider class of problems than traditional ML models (images being the canonical example). However, I thought where a like for like comparison was possible they tend to worse than gradient boosting.
Gradient boosting handles tabular data better than neural networks, often because the structure is simpler, and it becomes more of an issue to deal with the noise. You can do like-to-like comparisons between them for unstructured data like images, audio, video, text, and a well-designed NN will mop the floor with gradient boosting. This is because to handle that sort of data, you need to encode some form of bias around expected convolutional patterns in the data, or you won't get anywhere. Both CNNs and transformers do this.