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Mean Ensembling
- Takes the mean of the probability if the output classes
- Output is the class with maximum probability
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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
| 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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Mean Ensembling
- Takes the mean of the predicted outputs by each model
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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
| 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 |