Bid recommendation model for Fighting the Landlord based on multi-model stacking and feature extraction
Addressing the granularity limitation observed in existing research on"Fighting the Landlord"bidding decision problem, this paper proposes an approach for training a Bid Recommendation Model.Specifically, a methodology is devised for constructing hand features, including hand vector, hand pattern features, hand tidiness, minimum step in card play, and combination richness.Based on this, we propose a stacked approach to fuse the decision results of four base models and train a meta classifier CatBoost as the final decision model for the bidding decision.Our experimental results indicate that, in comparison with relying solely on hand vector features, this feature construction method significantly enhances model performance.Following the fusion of multiple model decisions through stacking, the accuracy of the second-layer model is further improved, achieving a precision of 84.3854％ on the test set.Moreover, this method provides some references for bidding decision in other card games.
Fighting the Landlordbid algorithmgame algorithmensemble learning
国家科技期刊平台
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