Machine learning challenge. The task for participants is to construct a prediction model that can learn how to accurately evaluate particular intra-game states.
AAIA'17 Data Mining Challenge is the fourth data mining competition organized within the framework of International Symposium Advances in Artificial Intelligence and Applications (https://fedcsis.org/2017/aaia).
The competition attracted 296 teams from 28 different countries. We got 8th place - top 2.7% result!
The task is to predict the probability of winning the game for evaluation quality moves of the AI player.
- Train – 2 000 000 + 1 250 000 (deprecated) objects (game stats)
- Test – 750 000 objects (game stats)
- Evaluation metric - AUC
public leaderboard – 5%, private leaderboard – 95%. Results link.
- Hearthstone - computer game using thematic cards.
- Basic data - current move, the number of crystals, player health, id Heroes, the number of cards.
- Data on the cards on the table of the player and the opponent (only minions) - the id of the cards, Attack, health, other abilities.
- Data on cards in the player's hand - id cards, card types (minions, spell, weapon), Attack, health, etc.
Xgboost features importance:
Final result is algorithms results blending using weighted arithmetic average:
0.3 * xgboostResult + 0.7 * logreResult