Lessons from homework
5
description
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 - PowerPoint PPT Presentation
Transcript of Lessons from homework
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– “Follow the data” rule
• 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– Reading: Mitchell, Chapter 10
• Evaluating hypotheses– Reading: Mitchell, Chapter 5
• Homework #2 assigned later today– Due 5:00PM October 23– Shorter than last time
Project Grading
• Questions– How did you encode your task? Why is this reasonable?– Which ML approaches? Why?– How did you evaluate your system?– Were you successful? Why or why not? What did/would you try
next?
• Grading based on:– 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?
