diff --git a/README.md b/README.md
index be16abc..776ddda 100644
--- a/README.md
+++ b/README.md
@@ -324,19 +324,19 @@ See the advanced scripts for additional details.
   Set a list of input files for training in `splitTypeTrain`, and a list of input files for testing in `splitTypeTest`. 
 
 - Most of the examples show the use of ascii input files. However, one may also use ROOT files as input (see `scripts/annz_rndReg_advanced.py`). For instance, one may define separate input files with corresponding ROOT Tree names, as
-```python
-glob.annz["inTreeNameTrain"]  = "tree0"  
-glob.annz["splitTypeTrain"]   = "file_0.root;file_1.root"
-glob.annz["inTreeNameTest"]   = "tree1"  
-glob.annz["splitTypeTest"]    = "file_2.root"
-```
-Notice that in this case, all files defined in `splitTypeTrain` must contain a ROOT Tree as defined in `inTreeNameTrain`, and the same goes for `splitTypeTest` and `inTreeNameTest`. Since the Tree names for training and testing can be different, one may e.g., use two separate Trees from a single input file for training and testing:
-```python
-glob.annz["splitTypeTrain"]   = "file_0.root"
-glob.annz["splitTypeTest"]    = "file_0.root"
-glob.annz["inTreeNameTrain"]  = "tree0"  
-glob.annz["inTreeNameTest"]   = "tree1"  
-```
+  ```python
+  glob.annz["inTreeNameTrain"]  = "tree0"  
+  glob.annz["splitTypeTrain"]   = "file_0.root;file_1.root"
+  glob.annz["inTreeNameTest"]   = "tree1"  
+  glob.annz["splitTypeTest"]    = "file_2.root"
+  ```
+  Notice that in this case, all files defined in `splitTypeTrain` must contain a ROOT Tree as defined in `inTreeNameTrain`, and the same goes for `splitTypeTest` and `inTreeNameTest`. Since the Tree names for training and testing can be different, one may e.g., use two separate Trees from a single input file for training and testing:
+  ```python
+  glob.annz["splitTypeTrain"]   = "file_0.root"
+  glob.annz["splitTypeTest"]    = "file_0.root"
+  glob.annz["inTreeNameTrain"]  = "tree0"  
+  glob.annz["inTreeNameTest"]   = "tree1"  
+  ```
 
 #### Definition of signal and background objects in single/randomized classification