Commit 52494095 authored by PauTheu's avatar PauTheu
Browse files


parent 48015cb6
......@@ -28,3 +28,11 @@ To tackle the concept drift problematic occuring in the testset, we combine seve
Each of the models is trained on a single region of wind turbines.
The models' predictions are combined by using a weighted majority vote.
More specifically, the models' output probabilities are acquired via softmax and averaged to form the ensemble's output.
## `Others.ipynb`
Data: use ten best features explored in data_exploration.ipynb file
### Approaches:
1. Lightgbm: leaf wise tree growth (best first) instead of level wise tree growth. low acc.
2. Easy NN: 2 hidden layers, batches 64, epochs 50, binary classification, Relu, ADAM. low acc.
3. TPOPT: looks for the best classification pipeline. low acc.
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