那个高手能解释下用sas做bp神经网络的结果解释啊?以及有些指标的值取值为多少时,比较好
Label | Train | Valid | Test |
[ TARGET=TYPE ] | | | |
Average Profit | 0.5 | 0.485714286 | 0.457142857 |
Misclassification Rate | 0.495238095 | 0.514285714 | 0.542857143 |
Average Error | 0.134431584 | 0.137557174 | 0.137147385 |
Average Squared Error | 0.134431584 | 0.137557174 | 0.137147385 |
Sum of Squared Errors | 141.1531635 | 48.14501105 | 48.00158467 |
Root Average Squared Error | 0.36664913 | 0.37088701 | 0.370334153 |
Root Final Prediction Error | 0.393844343 | | |
Root Mean Squared Error | 0.380489783 | 0.37088701 | 0.370334153 |
Error Function | 141.1531635 | 48.14501105 | 48.00158467 |
Mean Squared Error | 0.144772475 | 0.137557174 | 0.137147385 |
Maximum Absolute Error | 0.823276371 | 0.826041771 | 0.829326746 |
Final Prediction Error | 0.155113366 | | |
Divisor for ASE | 1050 | 350 | 350 |
Model Degrees of Freedom | 60 | | |
Degrees of Freedom for Error | 780 | | |
Total Degrees of Freedom | 840 | | |
Sum of Frequencies | 210 | 70 | 70 |
Sum Case Weights * Frequencies | 1050 | 350 | 350 |
Akaike's Information Criterion | -1378.187313 | | |
Schwarz's Baysian Criterion | -1094.1832 |
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