Use full names for sensitivity, specificity. Closes #66.

chapters/chapter3.md

@@ -255,7 +255,7 @@ The workflow `vote_wf` contains both the recipe and the model specification, and
 **Instructions**
 
 - Now fit the resamples `vote_folds` to the random forest model.
-- Compute the metrics `roc_auc`, `sens`, and `spec`.
+- Compute the metrics `roc_auc`, `sensitivity`, and `specificity`.
 
 <codeblock id="03_14_2">
 

exercises/exc_03_14_1.R

@@ -19,7 +19,7 @@ set.seed(234)
 glm_res <- vote_wf %>%
     ___(
         vote_folds,
-        metrics = metric_set(roc_auc, sens, spec),
+        metrics = metric_set(roc_auc, sensitivity, specificity),
         control = control_resamples(save_pred = TRUE)
     )
 

exercises/solution_03_14_1.R

@@ -19,7 +19,7 @@ set.seed(234)
 glm_res <- vote_wf %>%
     fit_resamples(
         vote_folds,
-        metrics = metric_set(roc_auc, sens, spec),
+        metrics = metric_set(roc_auc, sensitivity, specificity),
         control = control_resamples(save_pred = TRUE)
     )
 

exercises/solution_03_14_2.R

@@ -20,7 +20,7 @@ set.seed(234)
 rf_res <- vote_wf %>%
     fit_resamples(
         vote_folds,
-        metrics = metric_set(roc_auc, sens, spec),
+        metrics = metric_set(roc_auc, sensitivity, specificity),
         control = control_resamples(save_pred = TRUE)
     )
 

slides/chapter3_13.md

@@ -16,14 +16,14 @@ Let's talk about that model performance now, how to set _non-default performance
 vote_wf %>%
     fit_resamples(
         vote_folds,
-        metrics = metric_set(roc_auc, sens, spec),
+        metrics = metric_set(roc_auc, sensitivity, specificity),
         control = control_resamples(save_pred = TRUE)
     )
 ```    
 
 Notes: Just like in our first case study, we can use the function `fit_resamples()` to fit a model (a workflow in this case, actually, that holds both a preprocessor and a model specification) to each cross-validation fold and compute performance metrics. The code shown on this slide will fit our workflow `vote_wf` to the cross-validation folds in `vote_folds` and determine how well the model performed each time.
 
-The fitted models themselves are not kept or stored because they are only used for computing performance metrics. However, we are saving the predictions with `save_pred = TRUE` so we can build a confusion matrix, and we have also set _specific performance metrics_ to be computed (instead of the defaults) with `metric_set(roc_auc, sens, spec)`. We will have: 
+The fitted models themselves are not kept or stored because they are only used for computing performance metrics. However, we are saving the predictions with `save_pred = TRUE` so we can build a confusion matrix, and we have also set _specific performance metrics_ to be computed (instead of the defaults) with `metric_set(roc_auc, sensitivity, specificity)`. We will have: 
 
 - the area under the ROC curve, 
 - sensitivity, and