diff --git a/score_all_cfs.py b/score_all_cfs.py
index 2a9b665..9c9204b 100755
--- a/score_all_cfs.py
+++ b/score_all_cfs.py
@@ -15,8 +15,8 @@ def header(filter_name):
     for item in ["Normal", "Discret.", filter_name.upper()]:
         initial += f"{item:10s} "
         sec_line += "=" * 10 + " "
-    initial += "Reduction"
-    sec_line += "========="
+    initial += "Reduction Features selected"
+    sec_line += "========= " + "=" * 30
     print(initial)
     print(sec_line)
 
@@ -26,23 +26,24 @@ if len(sys.argv) > 1:
     filter_name = sys.argv[1]
 else:
     filter_name = "cfs"
-if filter_name not in ["cfs", "fcbs"]:
-    print("First parameter has to be one of: {cfs, fcbs}")
+if filter_name not in ["cfs", "fcbf"]:
+    print("First parameter has to be one of: {cfs, fcbf}")
 datasets = Datasets(False, False, "tanveer")
 header(filter_name)
 better = worse = equal = 0
 for dataset in datasets:
     mdlp = MDLP(random_state=1)
     X, y = datasets.load(dataset[0])
-    mfs = MFS()
+    mfs = MFS(discrete=True)
     now_disc = time.time()
     X_disc = mdlp.fit_transform(X, y)
+    # X_disc = X
     time_disc = time.time() - now_disc
     now_selec = time.time()
     if filter_name == "cfs":
         features_selected = mfs.cfs(X_disc, y).get_results()
     else:
-        features_selected = mfs.fcbs(X_disc, y, 5e-2).get_results()
+        features_selected = mfs.fcbf(X_disc, y, 5e-2).get_results()
     time_selec = time.time() - now_selec
     output = ""
     odte_score = stree_score = 0.0
@@ -60,5 +61,6 @@ for dataset in datasets:
     output = f"{dataset[0]:30s} {time_disc:7.3f} {time_selec:7.3f} "
     output += f"{score_norm:.8f} "
     output += f"{score_disc:.8f} {score_fs:.8f} "
-    output += f"{X.shape[1]:3} - {len(features_selected):3}"
+    output += f"{X.shape[1]:3} - {len(features_selected):3} "
+    output += f"{features_selected}"
     print(output)