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add notebook and description for video 8

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Kevin Markham 2015-07-16 01:07:29 -04:00
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@ -51,3 +51,10 @@ This repo contains IPython notebooks from my scikit-learn video series, as seen
- How does K-fold cross-validation overcome this limitation?
- How can cross-validation be used for selecting tuning parameters, choosing between models, and selecting features?
- What are some possible improvements to cross-validation?
8. Efficiently searching for optimal tuning parameters ([video](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/08_grid_search.ipynb), blog post)
- How can K-fold cross-validation be used to search for an optimal tuning parameter?
- How can this process be made more efficient?
- How do you search for multiple tuning parameters at once?
- What do you do with those tuning parameters before making real predictions?
- How can the computational expense of this process be reduced?