Kaggle Solution Walkthroughs: UM - Game-Playing Strength of MCTS Variants with Team Senkin13
From Kaggle
Learn about the winning approach by Team Senkin13 and the key techniques they used in UM - Game-Playing Strength of MCTS Variants competition. This competition tasked participants with creating a model to predict how well one Monte-Carlo tree search (MCTS) variant will do against another in a given game, based on a list of features describing the game. This challenge aims to help us figure out which MCTS variants work best in different types of games, so we can make more informed choices when...
Mentioned in This Episode
- CatBoost (product)
- Data Science (concept)
- Two-stage Modeling (concept)
- LightGBM (product)
- Leaderboard (concept)
- Feature Engineering (concept)
- Ensemble Methods (concept)
- Cross Validation (concept)
- Augmentation (concept)
- GBDT (concept)
- TF-IDF (concept)
- OOF Predictions (concept)
- Post-processing (concept)
- Feature Selection (concept)
- SVD (concept)
- Applied Mathematics (concept)
- XGBoost (product)