Model # | Framework | Data transformation steps |
---|---|---|
1 | XGBoost | Create Threshold One Hot Encoding (threshold = 30) for categorical/sparse features |
2 | LinearLearner | Converts features with extreme values to a uniform distribution Feature dimension reduction using PCA |
3 | LinearLearner | Scaling and centering features while accounting for data sparsity only |
4 | XGBoost | Create threshold one hot encoding (threshold = 5) for categorical/sparse features |
5 | LinearLearner | Create threshold one hot encoding (threshold = 6) for sparse features Feature dimension reduction using PCA |
6 | LinearLearner | Create threshold one hot encoding (threshold = 7) for categorical/sparse features |
7 | LinearLearner | Create threshold one hot encoding (threshold = 7) for categorical/sparse features Feature dimension reduction using PCA |
8 | XGBoost | Create threshold one hot encoding (threshold = 7) for categorical/sparse features |
9 | XGBoost | Create threshold one hot encoding (threshold = 9) for categorical/sparse features |
10 | MLP | Scaling and centering features while accounting for data sparsity only |