diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index dbf83cf..25b7231 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -3,5 +3,6 @@
   <component name="VcsDirectoryMappings">
     <mapping directory="$PROJECT_DIR$" vcs="Git" />
     <mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/rapids/calculatingfeatures" vcs="Git" />
   </component>
 </project>
\ No newline at end of file
diff --git a/presentation/StressfulEvents.Rmd b/presentation/StressfulEvents.Rmd
index 992e848..5f3039d 100644
--- a/presentation/StressfulEvents.Rmd
+++ b/presentation/StressfulEvents.Rmd
@@ -12,6 +12,7 @@ knitr::opts_chunk$set(
 ```{r libraries, include=FALSE}
 library(knitr)
 library(kableExtra)
+library(stringr)
 library(RColorBrewer)
 library(magrittr)
 library(tidyverse)
@@ -77,4 +78,26 @@ podatki %>%
 # Problem description
 
 We are trying to predict whether a stressful event occurred, i.e. stressfulness > 0, or not (stressfulness == 0).
-First
+First, set up a leave-one-subject-out validation and use original distribution of the class variable.
+
+For this, the majority classifier has a mean accuracy of 0.85 (and median 0.90), while the F1-score, precision and recall are all 0.
+
+We also have an option to validate the results differently, such as with "half-loso", i.e. leaving half of the subject's data in the training set and only use half for testing, or k-fold cross-validation.
+Additionally, we can undersample the majority class to balance the dataset.
+
+# Results
+## Leave one subject out, original distribution
+
+```{r event_detection}
+scores <- read_csv("event_stressful_detection_loso.csv", col_types = "ffdd")
+scores_wide <- scores %>% 
+  select(!max) %>% 
+  pivot_wider(names_from = metric, 
+              names_sep = "_",
+              values_from = mean) %>% 
+  rename_all(~str_replace(.,"^test_",""))
+kable(scores_wide, digits = 2) %>% 
+  column_spec(4, color = 'white', background = 'black') %>% 
+  kable_styling(full_width = TRUE)
+```
+