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# Lessons from homework PowerPoint PPT Presentation

Lessons from homework. Try the simplest thing first “Occam’s Razor”: Prefer the simplest hypothesis that fits the data Corresponds to the decision tree bias Shown to be useful empirically (various mostly unsatisfying philosophical justifications also exist) “Laziness” rule

Lessons from homework

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### Lessons from homework

• Try the simplest thing first

• “Occam’s Razor”: Prefer the simplest hypothesis that fits the data

• Corresponds to the decision tree bias

• Shown to be useful empirically (various mostly unsatisfying philosophical justifications also exist)

• “Laziness” rule

• If it works, you’re done

• If it doesn’t work, you learn how to proceed

• “Justify yourself” rule

• Your audience/boss/customer will resist a complex model unless you’ve shown simple ones are inadequate

### This week

• Rule learning

• Evaluating hypotheses

• Homework #2 assigned later today

• Due 5:00PM October 23

• Shorter than last time

• Questions

• Which ML approaches? Why?

• How did you evaluate your system?

• Were you successful? Why or why not? What did/would you try next?

• Thoroughness of evaluation

• Understanding of ML issues (e.g. overfitting, inductive bias, etc.)

• Quality of presentation

• Not on ultimate performance of your system

### How to formulate an ML task

• Example: Web pages

• Classify as Student, Instructor, Course

• What are the input features?

• Would you use DTs or NNs?

• Example: Face Recognition

• Identify as one of 20 people

• What are the input features?

• DTs or NNs?