My experience with tabular data textbooks is that they introduce many different techniques, in a relatively un-opinionated way, which doesn't help give you intuitions on what your strategy for real-world problems should be. In practice, almost all tabular data problems I've encountered boil down to:
1. Make sure you don't have any impossible nonsense or data leakage in your features or target
2. Split your data in an intelligent way (temporally for time-series, group-wise for hierarchical data)
3. Try a really simple linear regression / logistic regression model to get a "dumb" baseline for your accuracy/error metric and make sure it is reasonable
4. If you need interpretability, consider a GAM, else, throw it into XGBoost and you'll get state of the art results
1. Make sure you don't have any impossible nonsense or data leakage in your features or target
2. Split your data in an intelligent way (temporally for time-series, group-wise for hierarchical data)
3. Try a really simple linear regression / logistic regression model to get a "dumb" baseline for your accuracy/error metric and make sure it is reasonable
4. If you need interpretability, consider a GAM, else, throw it into XGBoost and you'll get state of the art results