PACKT has just posted an sampling of four recipes that I curated from the entire book. I think they are a fun sampling. Here I’ve written a little bit about my rationale for choosing the recipes that I did. Enjoy.

From Chapter Two, Data Preparation: Select I’ve chosen Using the Feature Selection node creatively to remove, or decapitate, perfect predictors, to illustrate this. It happens to be one of mine. It is not difficult, but it uses a key feature in an unexpected way.

From Chapter 6, Selecting and Building a Model,  Next-best-offer for large data sets is our representative of the pushing the limits category. Most of the documentation of his subject uses a different approach that while workable on smaller data sets, is not scalable. We were fortunate to have Scott Mutchler contribute this recipe in addition to his fine work in Chapter 8.

From Chapter Seven, Modeling – Assessment, Evaluation, Deployment, and Monitoring, Correcting a confusion matrix for an imbalanced target variable by incorporating priors, by Dean Abbott, is a great example of the unexpected. The Balance Node is not the only way to deal with an out of balance target. Also from Chapter Seven, I’ve chosen Combining generated filters. This short recipe definitely invokes that reaction of “I didn’t know you could do that!” It was provided by Tom Khabaza.