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Competition summary
Miłosz Michta edited this page Sep 3, 2018
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- Interest rate feature * *
- Adding PCA, T-SNE, Denoising autoencoders * for feature extraction *
- Build model to fill NaN's
- Dedicate more time for neural networks like RNN, 1-D Convolution for time series *
- Using oof prediction from experiments evaluated by grid/random/bayesian search *
- Trust your CV!
check our GitHub organization https://github.com/neptune-ml for more cool stuff 😃
Kamil & Kuba, core contributors
- chestnut 🌰: LightGBM and basic features
- seedling 🌱: Sklearn and XGBoost algorithms and groupby features
- blossom 🌼: LightGBM on selected features
- tulip 🌷: LightGBM with smarter features
- sunflower 🌻: LightGBM clean dynamic features
- four leaf clover 🍀: Stacking by feature diversity and model diversity