Knowing which algorithms work well for different data types is useful, but it doesn't answer the real question you face in production: which model should you actually use?

Model selection is about trade-offs. Accuracy, latency, training cost, interpretability, data requirements, and operational complexity all matter. Improving one often makes another worse.

The goal is not to pick the most powerful model. It is to justify each increase in complexity with a clear, measurable gain.