Interesting talk about how ensemble learning was used in the Netflix contest and a short presentation on statistical bootstrapping.
- The basic idea is that multiple models are used and they are fitted to the data they work best with.
- Usually, we compare models and take the best one but what if instead we combine different models and take the best characteristics of each?
- Cross-validation - using a portion of the sample data to check of over-fitting.