Week 6 — Lesson 5: Feature importance, error analysis, the final test

3 min

A score of 0.93 is not the end of the NorthPeak project. Before trusting it, you ask which columns the forest used, and where it fails, by machine type and by month. Then you open the October to December test set once, with the settings fixed by cross-validation, and write the numbers down. This lesson covers feature importance, error analysis and that single use of the test set, where the tuned forest scores 0.936 and the Week 5 logistic regression 0.961.

Preview — the rest of the lesson is for enrolled readers.

Already enrolled with a code?

Your access is tied to your account, not to this link. Sign in with the same email you used in class: your course is waiting, no need to enter the code again.

Sign inNo account yet? Create one
This lesson is part of the “Week 6 — Model selection and validation” module

The first modules of the course are open to everyone. For the rest you have three options: buy this course once and for all, subscribe, or enter the code handed out in class.

Are you a student on this course?

The code is tied to your account: sign in or create an account and it will be applied automatically when you come back.

No account yet? Create one