FES
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Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson
1. In Chapter 11.2.1, there is a mention of results from an initial model: >The initial analysis of the training set showed that there were 5 predictors with an area...
the subset recipe was not being used in the subset training model
Version 2019-06-21, last paragraph of section 2.2: ``` ... red boxes along the diagonal of the corrleation matrix in Figure 2.3. ```
In the section [7_04_The_Brute-Force_Approach_to_Identifying_Predictive_Interactions](https://bookdown.org/max/FES/complete-enumeration.html), the code [ames_pairwise.R](https://github.com/topepo/FES/blob/06812c48a21882808403cee338b8312fdbd35a46/07_Detecting_Interaction_Effects/7_04_The_Brute-Force_Approach_to_Identifying_Predictive_Interactions/ames_pairwise.R) the particular chunk of codes as shown below are throwing some errors, ```r for (i in 1:nrow(interactions)) { tmp_vars
Near the end of Section 3.4.1 the statement is made that leave-one-out cross validation is deprecated. Because this version is the most compute-intensive of all (training is done most often,...
In section 3.4.1, the suggestion is made that in order to compute a performance criterion using V-fold cross validation, the criterion should be computed separately for each fold, and then...
It appears that averaging RMSE (rather than MSE) over V-folds introduces a small bias to the test set error estimate. Averaging RMSE over V-folds appears to underestimate the "true" test...
Section 3.4.1 discusses R repeats of V-fold cross validation and incorrectly states that the variance reduction will be by a factor of Sqrt(R). This would be true only if the...
Thanks for giving us feedback on the book. Please follow these guidelines: ## Comments or questions on the content Hello I have been trying to recreate the plots that are...
Hi, The codes used to generate Figures 6.4, 6.5, 6.6 are not available at Github. Could you please make those codes available? Thanks.