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add blog post link for video 7

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Kevin Markham 2015-06-30 12:19:12 -04:00
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@ -46,7 +46,7 @@ This repo contains IPython notebooks from my scikit-learn video series, as seen
- What are some evaluation metrics for regression problems?
- How do I choose which features to include in my model?
7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/07_cross_validation.ipynb), blog post)
7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/07_cross_validation.ipynb), [blog post](http://blog.kaggle.com/2015/06/29/scikit-learn-video-7-optimizing-your-model-with-cross-validation/))
- What is the drawback of using the train/test split procedure for model evaluation?
- 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?