Ensembling_Techniques

Ensembling for multiclass classification problems

Methods Used

1. Mean Ensembling

   • Takes the mean of the probability if the output classes
   • Output is the class with maximum probability

2. Majority Voting

   • Assigns as output whichever class has maximum number of votes among ensemble models
   • In case of a tie, takes the first group of tied classes

Results

Model	Accuracy
RESNET50	0.969882729211
XCEPTION	0.945895522388
CAPSNET	0.653251599147
CNN_CUSTOM	0.861407249467
MAJORITY VOTING	0.967750533049
MEAN ENSEMBLE	0.982675906183

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Ensembling for regression problems

Methods Used

1. Mean Ensembling

   • Takes the mean of the predicted outputs by each model

2. Stacking

   • Takes the output of the ensembles and passed it through a MLP Regressor
   • Output is the output provided by the regressor on passing the test data

Results

Model	MSE	R2_Score
MLP	0.0103210967873	0.808796324494
RBF	0.0106966198534	0.801839564767
MEAN ENSEMBLE	0.00845186510476	0.843424811702
STACKING ENSEMBLE	0.00729725486593	0.864814565717