Is accuracy enough for determining model performance....?
hello
I have 3 predictive models... those are
1. Backpropagation-Based
2. C4.5-Based
3. CN2-Based.
I use accuracy for predictive model performance measurement... and these were their result :
1. Backpropagation ==> 83.14% on Training, 85.12 on Testing
2. C4.5 ==> 83.72 % on Training, 84,04% on Testing.
3. CN2 ==> 82.98 % on Training, 84,65 % on Testing.
when I look at the percentage of accuracy of each algorithm, It means that there is no significant difference between one and the others. My question is that accuracy enough to determine or judge the performance of certain algorithm in certain case? if that so, I just wonder which is the best model among that three model, because... you know, there are no real significant difference between them ::)... ??? (because it could be only 1 or 2 correctly classified vector...)
thank you for your advice,
regards
Dimas Yogatama...
I have 3 predictive models... those are
1. Backpropagation-Based
2. C4.5-Based
3. CN2-Based.
I use accuracy for predictive model performance measurement... and these were their result :
1. Backpropagation ==> 83.14% on Training, 85.12 on Testing
2. C4.5 ==> 83.72 % on Training, 84,04% on Testing.
3. CN2 ==> 82.98 % on Training, 84,65 % on Testing.
when I look at the percentage of accuracy of each algorithm, It means that there is no significant difference between one and the others. My question is that accuracy enough to determine or judge the performance of certain algorithm in certain case? if that so, I just wonder which is the best model among that three model, because... you know, there are no real significant difference between them ::)... ??? (because it could be only 1 or 2 correctly classified vector...)
thank you for your advice,
regards
Dimas Yogatama...