diff --git a/src/AI/DecisionTrees/TestDecisionTree.py b/src/AI/DecisionTrees/TestDecisionTree.py
new file mode 100644
index 0000000..316b6b6
--- /dev/null
+++ b/src/AI/DecisionTrees/TestDecisionTree.py
@@ -0,0 +1,39 @@
+import random
+
+from typing import List
+
+from src.AI.DecisionTrees.DecisionTreeExample import DecisionTreeExample
+from src.AI.DecisionTrees.InductiveDecisionTreeLearning import inductiveDecisionTreeLearning
+from src.AI.DecisionTrees.projectSpecificClasses.SurvivalAttributesDefinitions import SurvivalAttributesDefinitions
+from src.AI.DecisionTrees.projectSpecificClasses.SurvivalClassification import SurvivalClassification
+
+
+def testDecisionTree(examples: List[DecisionTreeExample], iterations=10, partOfExamplesAsTrainingSet=0.9):
+    examplesNum = len(examples)
+    trainingSetSize = int(examplesNum * partOfExamplesAsTrainingSet)
+    testSetSize = examplesNum - trainingSetSize
+
+    treeScores = []
+
+    for i in range(iterations):
+        # Shuffling examples
+        random.shuffle(examples)
+        # Test and training set
+        trainingSet = examples[:trainingSetSize]
+        testSet = examples[trainingSetSize:]
+
+        # Create decision tree out of training set
+        dt = inductiveDecisionTreeLearning(trainingSet, SurvivalAttributesDefinitions.allAttributesDefinitions,
+                                           SurvivalClassification.FOOD, SurvivalClassification)
+
+        # Check how many answers will be correct for test set
+        correctAnswers = 0
+        for testExample in testSet:
+            dtAnswer = dt.giveAnswer(testExample)
+            if dtAnswer == testExample.classification:
+                correctAnswers += 1
+
+        treeScores.append(correctAnswers / testSetSize)
+
+    return treeScores
+