# ML Wiki

## Prioritizing

• Suppose we want to build a spam classifier
• How we should spend time to do that?

Options:

• Collect more data to have more samples
• Develop sophisticated features based on email routing etc
• Develop sophisticated algorithm to detect misspelled words such as m0rtgage etc

How to choose what is better?

## Error Analysis

Recommended Approach

• Start with the simplest possible algorithm (avoid premature optimization!) that you can implement quickly
• Plot Learning Curves to decide if more data features is likely to help
• Do the Error Analysis
• Manually examine the examples (in your CV set) that your algorithm misclassified.
• See if you spot any systematic trend in what types of examples it makes errors on

### Example

$m_{\text{cv}} = 500$, and our algorithm misclassifies 100 of them

so we manually examine the 100 errors and categorize them based on

• what type of email it is
• pharmacy 12
• replica 4
• steal password 53 - seems it's worth investing time in this category!
• other 31
• what features might help the algorithm to classify it correctly
• deliberate misspelling 15
• unusual email routing 16
• unusual punctuation 32 - so concentrate on this!

## Numerical Evaluation

• Error Analysis may not be helpful for deciding if this is likely to improve performance
• The only solution in this case is to try it and see if it works
• But we need a numerical evaluation (e.g. Cross-Validation error) of algorithm's performance with and without the new code/idea/etc
• So we need to use Error Metrics