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add blog link for video 8

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Kevin Markham 2015-07-18 16:18:21 -04:00
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@ -52,7 +52,7 @@ This repo contains IPython notebooks from my scikit-learn video series, as seen
- 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)
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](http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/))
- 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?