In 1-2 paragraphs, explain your decision tree model (interpret at least 2 complete rules from the decision tree). This question is from Business Analytics Applications

Tree

CarInsurance > 0.500 | Balance > -208 | | Age > 23.500 | | | Balance > 20901: success {failure=0, success=3} | | | Balance  20901 | | | | Balance > 11747: failure {failure=2, success=0} | | | | Balance  11747 | | | | | Balance > 7754.500: success {failure=0, success=4} | | | | | Balance  7754.500 | | | | | | Age > 58.500: success {failure=7, success=37} | | | | | | Age  58.500 | | | | | | | Age > 24.500: success {failure=106, success=143} | | | | | | | Age  24.500: failure {failure=2, success=1} | | Age  23.500: success {failure=0, success=7} | Balance  -208: failure {failure=2, success=0} CarInsurance  0.500 | Age > 74.500: failure {failure=1, success=1} | Age  74.500 | | Balance > -0.500 | | | Age > 26.500 | | | | Age > 28.500 | | | | | Age > 30.500 | | | | | | Age > 31.500: failure {failure=136, success=14} | | | | | | Age  31.500 | | | | | | | Balance > 1451 | | | | | | | | Balance > 1851: failure {failure=2, success=0} | | | | | | | | Balance  1851: success {failure=0, success=2} | | | | | | | Balance  1451: failure {failure=3, success=0} | | | | | Age  30.500: failure {failure=7, success=0} | | | | Age  28.500: failure {failure=4, success=2} | | | Age  26.500: failure {failure=4, success=0} | | Balance  -0.500: failure {failure=16, success=0}