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munitech4u
Quartz | Level 8

I am trying to create ensemble of 3 models: 

1. Regression, cumulative events percentage captured at 20%: 56.8

2. Neural Network, cumulative events percentage captured at 20%: 59.3

3. SVM, cumulative events captured at 20%: 54.01

3. Decision Tree, cumulative events captured at 20%: 48.05

 

Ensemble, cumulative results captured at 20%: 54.18

 

My target is binary. and In ensemble, I have set the posterior probability to maximum. So it will take the maximum probability out of all models, I believe.

3 REPLIES 3
M_Maldonado
Barite | Level 11

Hi Munitech,

Right, maximum will take the maximum posterior probability of your models.

What is the misclassification for your 3 models and their ensemble?

 

Thanks,

M

munitech4u
Quartz | Level 8
Regression: Train>0.012276, Test: 0.012101
SVM: Train>0.012515, Test: 0.012374
Decision Tree: Train>0.012245, Test: 0.012069
NN: Train: Train>0.01227, Test: 0.012095
M_Maldonado
Barite | Level 11

My bad, I don't know how to count... I meant to ask, the Misc of your 4 models and their ensemble.

I am curious if the ensemble of all 4 models is getting worse--and if the Tree or the SVM are tripping it off...

 

Since you are at it, can you include the classification charts as well?

Maybe that will give us a better light of what's going on.

 

Thanks!

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