I'll be doing a morning tutorial at useR! at the end of June in Los Angeles. I've done this same presentation at the last few conferences and this will probably be the last time for this specific workshop.
I will be including a copy of the book for those who take the tutorial and all the proceeds (minus book costs) will be donated to the Foundation for Open Access Statistics (FOAS).
The tutorial outline is:
- Conventions in R
- Data splitting and estimating performance
- Data pre-processing
- Over-fitting and resampling
- Training and tuning tree models
- Training and tuning a support vector machine
- Comparing models (as time allows)
- Parallel processing (as time allows)
I'm also giving a talk called "Adaptive Resampling in a Parallel World":
Many predictive models require parameter tuning. For example, a classification tree requires the user to specify the depth of the tree. This type of "meta parameter" or "tuning parameter" cannot be estimated directly from the training data. Resampling (e.g. cross-validation or the bootstrap) is a common method for finding reasonable values of these parameters (Kuhn and Johnson, 2013). Suppose B resamples are used with M candidate values of the tuning parameters. This can quickly increase the computational complexity of the task. Some of the M models could be disregarded early in the resampling process due to poor performance. Maron and Moore (1997) and Shen el at (2011) describe methods to adaptively filter which models are evaluated during resampling and reducing the total number of model fits. However, model parameter tuning is an "embarrassingly parallel" task; model fits can be calculated across multiple cores or machines to reduce the total training time. With the availability of parallel processing is it still advantageous to adaptively resample?
This talk will briefly describe adaptive resampling methods and characterize their effectiveness using parallel processing via simulations.