How do we adapt the model to explain more variability in the data?
Do we give it more information? (i.e. another variable, more data to train on...)
Or do we change the structure of the model? Increase the complexitity...
You can always improve a model but there are real-world considerations: time, expense, expertise.
Improvement isn't a one-dimensional thing: higher precision, higher out-of-sample accuracy, better clarity of communication? Parsimony is often desirable (especially in theoretical models).
Practicalities: benchmarking, book-keeping & version control.
Models are always wrong. Model evaluation is about understanding why your model is wrong and whether the level of incorrectness is acceptable
Real-world significance of models. Think about how your data is structured, remember these are real people.
Should not treat everyone the same.
Multilevel models?
Estimation
In this module we need to give the students the tools they need to improve upon the model in their hands-on session. If we choose to include the concept of multi-level modelling then we will need more time. 12-16 hrs
Description
Estimation
In this module we need to give the students the tools they need to improve upon the model in their hands-on session. If we choose to include the concept of multi-level modelling then we will need more time. 12-16 hrs