Comments (1)
garethjns
commented on May 30, 2024
Hi @YasminAMassoud, that figure was generated manually, I don't think the code is anywhere in the repo. It shows the mean importance (either gain or number of splits, I forget) for each feature in the random forest model. The top figure is for all the features, unlabelled but grouped by window size. The bottom figure are the top 20 features, with labels.
from kaggle-eeg.
Related Issues (18)
- train.m error HOT 3
- Question on interpretation of output results HOT 5
- Original Kaggle data HOT 1
- Running the code HOT 4
- Features Object_checkFiles
- Run Time HOT 3
- Solution File HOT 5
- Training and Testing HOT 5
- Training and Testing
- Private AUC is different HOT 1
- Feature information not saved in seizureModels
- Model feature names
- Training two SVMs instead of SVM and RBT HOT 1
- Redundant import methods in featuresObject
- Data-sub path setting is set in featuresObject
- Temporary change to data sub paths HOT 1
- Predict.m error HOT 2
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from kaggle-eeg.