A couple numbers wrong in the https://www.tmwr.org/performance.html#performance-metrics-and-inference.

[RaymondBalise]

The numbers in the paragraph before the note in Section 9.1 in the online rendered version of TMWR (https://www.tmwr.org/performance.html#performance-metrics-and-inference) do not match the printed book (the second to last paragraph on page 113) or the code that is produced by the repo (line 110 of https://github.com/tidymodels/TMwR/blob/main/09-judging-model-effectiveness.Rmd).

The physical book says and when I spot checked the repo produces:

we can estimate the accuracy of this model to be about 💥73.3%💥. Accuracy is often a poor measure of model performance; we use it here because it is commonly understood. If the model has 💥73.3%💥 fidelity to the data, should we trust conclusions it produces? We might think so until we realize that the baseline rate of nonimpaired patients in the data is 72.7%. This means that, despite our statistical analysis, the two-factor model appears to be only 💥0.6%💥 better ...

The book website currently shows:

we can estimate the accuracy of this model to be about 💥72.7%💥. Accuracy is often a poor measure of model performance; we use it here because it is commonly understood. If the model has 💥72.7%💥 fidelity to the data, should we trust conclusions it produces? We might think so until we realize that the baseline rate of nonimpaired patients in the data is 72.7%. This means that, despite our statistical analysis, the two-factor model appears to be only 💥0%💥 better ...

I didn't pull the repo to test but I think you just need to rerender the current files to fix the online version of the book.

[juliasilge]

I currently get 73.1% accuracy, using the code we have in Ch 9:

library(tidymodels)
data(ad_data)

set.seed(245)
ad_folds <- vfold_cv(ad_data, repeats = 5)

logistic_reg() %>% 
  fit_resamples(Class ~ (Genotype + male + age)^2, ad_folds) %>%
  collect_metrics()
#> ! Fold01, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold01, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold02, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold02, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold03, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold03, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold04, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold05, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold05, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold06, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold06, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold07, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold07, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold08, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold08, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold09, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold09, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold10, Repeat1: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold10, Repeat1: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold01, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold01, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold02, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold02, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold03, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold03, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold04, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold04, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold05, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold05, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold06, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold07, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold07, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold08, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold08, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold09, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold09, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold10, Repeat2: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold10, Repeat2: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold01, Repeat3: preprocessor 1/1, model 1/1: glm.fit: algorithm did not converge, glm.fit: fitted probabilities numer...
#> ! Fold01, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold02, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold02, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold03, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold03, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold04, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold04, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold05, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold05, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold06, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold06, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold07, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold07, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold08, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold08, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold09, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold10, Repeat3: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold10, Repeat3: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold01, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold01, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold02, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold02, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold03, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold03, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold04, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold04, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold05, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold05, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold06, Repeat4: preprocessor 1/1, model 1/1: glm.fit: algorithm did not converge, glm.fit: fitted probabilities numer...
#> ! Fold06, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold07, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold08, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold08, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold09, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold09, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold10, Repeat4: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold10, Repeat4: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold01, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold01, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold02, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold02, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold03, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold03, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold04, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold04, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold05, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold05, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold06, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold06, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold07, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold07, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold08, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold08, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold09, Repeat5: preprocessor 1/1, model 1/1: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ! Fold09, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> ! Fold10, Repeat5: preprocessor 1/1, model 1/1 (predictions): prediction from a rank-deficient fit may be misleading
#> # A tibble: 2 × 6
#>   .metric  .estimator  mean     n std_err .config             
#>   <chr>    <chr>      <dbl> <int>   <dbl> <chr>               
#> 1 accuracy binary     0.731    50 0.00978 Preprocessor1_Model1
#> 2 roc_auc  binary     0.691    50 0.0159  Preprocessor1_Model1

^{Created on 2022-09-01 with reprex v2.0.2}

I wonder if we are missing a seed somewhere? Or is this a cache issue?

[RaymondBalise]

Given that the wrong numbers are both 72.7% (which is the correct baseline number), I am guessing that the inline code chunk, which was used to render the online version of the book, was just looking up the wrong value. In theory somebody could check the blame/history to see if that was the root of the problem.... I think if the book is rendered, using the current code, it should fix itself. 🤞