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