diff --git a/aufgaben/p5/pla_applied.py b/aufgaben/p5/pla_applied.py
index 306838d666b4049300f6ff0a57646d5400730258..e8c6518090f009e5d7324d11df20c691deffdd47 100644
--- a/aufgaben/p5/pla_applied.py
+++ b/aufgaben/p5/pla_applied.py
@@ -1,3 +1,5 @@
+import math
+
 from algorithm.pla.perceptron_learning_algorithm import train, train_pocket
 from algorithm.pla.perceptron import Perceptron
 from algorithm.pla.transfer_functions import normalized_tanh
@@ -26,10 +28,37 @@ def apply_pla():
     max_iterations = 100
     learning_rate = 0.10
     train(perceptron, training_data, max_iterations, learning_rate)
-    print("\nPLA: Zunächst Gewichte, dann Klassifizierung. Erwartet: 1 & 0")
+    print("\nPLA: Zunächst Gewichte, dann Klassifizierung. Gehen = 1, Kniebeuge = 0")
     print(perceptron.weights)
-    print(perceptron.classify(joggen.werte[15:18].tolist()))
-    print(perceptron.classify(kniebeuge.werte[55:58].tolist()))
+    cnt_true_pos = 0
+    cnt_true_neg = 0
+    cnt_false_pos = 0
+    cnt_false_neg = 0
+    unclassified = 0
+    for i in range(500):
+        jog_res = perceptron.classify(joggen.werte[i:i+3].tolist())
+        knie_res = perceptron.classify(kniebeuge.werte[i:i+3].tolist())
+        if math.isclose(1, jog_res, abs_tol=0.1):
+            cnt_true_pos += 1
+        elif math.isclose(0, jog_res, abs_tol=0.1):
+            cnt_false_neg += 1
+        else:
+            unclassified += 1
+        if math.isclose(0, knie_res, abs_tol=0.1):
+            cnt_true_neg += 1
+        elif math.isclose(1, knie_res, abs_tol=0.1):
+            cnt_false_pos += 1
+        else:
+            unclassified += 1
+    print(f"""
+    Anzahl Prüfungen: 1000
+    True Positive: {cnt_true_pos}
+    False Positive: {cnt_false_pos}
+    True Negative: {cnt_true_neg}
+    False Negative: {cnt_false_neg}
+    Unclassifiable: {unclassified}
+    Fehlerrate: {(cnt_false_neg+cnt_false_pos) / 10}% bzw. {(cnt_false_neg+cnt_false_pos+unclassified) / 10}%
+    """)
 
 
 def apply_pocket():
@@ -54,10 +83,37 @@ def apply_pocket():
     max_iterations = 1
     learning_rate = 0.10
     train_pocket(perceptron, training_data, max_iterations, learning_rate)
-    print("\nPocket: Zunächst Gewichte, dann Klassifizierung. Erwartet: 1 & 0")
+    print("\nPocket: Zunächst Gewichte, dann Klassifizierung. Gehen = 1, Kniebeuge = 0")
     print(perceptron.weights)
-    print(perceptron.classify(joggen.werte[15:18].tolist()))
-    print(perceptron.classify(kniebeuge.werte[55:58].tolist()))
+    cnt_true_pos = 0
+    cnt_true_neg = 0
+    cnt_false_pos = 0
+    cnt_false_neg = 0
+    unclassified = 0
+    for i in range(500):
+        jog_res = perceptron.classify(joggen.werte[i:i+3].tolist())
+        knie_res = perceptron.classify(kniebeuge.werte[i:i+3].tolist())
+        if math.isclose(1, jog_res, abs_tol=0.1):
+            cnt_true_pos += 1
+        elif math.isclose(0, jog_res, abs_tol=0.1):
+            cnt_false_neg += 1
+        else:
+            unclassified += 1
+        if math.isclose(0, knie_res, abs_tol=0.1):
+            cnt_true_neg += 1
+        elif math.isclose(1, knie_res, abs_tol=0.1):
+            cnt_false_pos += 1
+        else:
+            unclassified += 1
+    print(f"""
+    Anzahl Prüfungen: 1000
+    True Positive: {cnt_true_pos}
+    False Positive: {cnt_false_pos}
+    True Negative: {cnt_true_neg}
+    False Negative: {cnt_false_neg}
+    Unclassifiable: {unclassified}
+    Fehlerrate: {(cnt_false_neg+cnt_false_pos) / 10}% bzw. {(cnt_false_neg+cnt_false_pos+unclassified) / 10}%
+    """)
 
 
 if __name__ == '__main__':