Model selection

How this question breaks down

1. 1BaselinesCore14 minA model's score means nothing until it sits next to what you would get without it, a constant, a rule, a simple model, the system already in production. Build that ladder first, on the same data and metric, and keep the cheapest rung as the fallback.
2. 2Trees vs netsCore16 minBoosted trees split a table of meaningful columns into regions and are the default for tabular prediction; neural networks learn smooth functions over embeddings and win when inputs are raw text, images, sequences or millions of IDs, and when the data and the team can scale. Most large systems end up using both.
3. 3CapacityCore14 minCapacity is how much a model can fit. Too little and it misses real patterns; too much for the data and it memorises noise. Learning curves tell you which, and the fix differs: more capacity or features for the first, more data, fewer parameters per example, regularisation or early stopping for the second.

Work through the parts in order and you’ve covered the full design. Each part is one topic.