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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

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