• What is a model?
  a summarized representation of the training data

Base classifiers

• Bayes

Ensemble classifiers

• Nearest-neighbor classifier does not generate a model.

Considerations

• Qualiticative attributes

Practical issues

• Imbalanced class distributions
  • $99%$ of all instances are from negative class
  • Stratification
    • undersample the larger class
    • oversample the smaller class
  • Other approches
    • Assign higher penalty to mispredicting instances from smaller class
    • Generate more data by purtutbing smaller class

Approches for big-data

• Randomly sample a subset of the data
• Repeat the sampling $B$ times
• Combine the classifiers