= Jan 12, 2017 =
[[/Attendees]]

== Review of Chapter 2 ==
Chatted about various highlights of chapter 2.  Went through several of the R examples that we hadn't seen before, especially `contour`, `persp`, and `identify`.

 * Where (in the domain of the data) is the model useful?
 * Prediction versus inference.  One opinion is that a good prediction model also ought to be explainable like a good inference model.
 * Parametric versus non-parametric.  Parametric is great whenever you can determine the shape of your data; otherwise, non-parametric may be the only way to go.

Had a side-discussion about Lasso.  David had read a paper demonstrating that using Lasso to select the best variables to include in a model uses one degree of freedom per variable, in order, within about 6 significant figures.  This is contrary to the belief that selecting variables would be like conducting consecutive experiments, and require some ''p''-value adjustments (see the Green Jelly Bean XKCD comic).

Back to chapter 2:

 * ISLR's treatment of model accuracy in train versus test is top-notch.
 * Its discussion about the bias/variance trade-off is also good.

== Next Time ==
 * Thursday, February 9, 2017
 * Be ready to discuss Chapter 3 of AnIntroductionToStatisticalLearning