Choosing a Model Family on Noisy Data
"Which models would you try on this problem, and why?" This lesson pulls together the three families the course has taught. Regularised linear models are in ridge, lasso and elastic net. Tree ensembles are in decision trees and bagging and boosting and gradient boosted trees. Neural networks are in neural networks in brief.
A complete answer says what each family assumes about the signal, what it needs from the data, and how you would decide between them without fooling yourself. The next lesson, the machine learning round, fits this into the structure of a full interview answer.
What the three families assume
The families differ in what shape of signal they can represent, and in how much data they need before that flexibility helps rather than hurts.
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