Select Language

AI社区

公开数据集

扑克手分类

扑克手分类

23.99M
137 浏览
0 喜欢
0 次下载
0 条讨论
Games,Multiclass Classification,Card Games Classification

数据结构 ? 23.99M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Poker Hand Classification This is a simple data set in which one can classify what poker hand has been dealt given 5 cards. Each card is given as a 2 attributes, a suit (Hearts, Spades, Diamonds, Clubs) and a rank (1-13). The prediction is an attribute 0-9 describing what hand was shown (See Poker Hands). An instance of training data is 11 attributes: 1) S1 "Suit of card #1" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 2) C1 "Rank of card #1" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 3) S2 "Suit of card #2" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 4) C2 "Rank of card #2" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 5) S3 "Suit of card #3" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 6) C3 "Rank of card #3" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 7) S4 "Suit of card #4" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 8) C4 "Rank of card #4" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 9) S5 "Suit of card #5" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 10) C5 "Rank of card 5" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 11) CLASS "Poker Hand" Ordinal (0-9) Poker Hands 0: Nothing in hand; not a recognized poker hand 1: One pair; one pair of equal ranks within five cards 2: Two pairs; two pairs of equal ranks within five cards 3: Three of a kind; three equal ranks within five cards 4: Straight; five cards, sequentially ranked with no gaps 5: Flush; five cards with the same suit 6: Full house; pair + different rank three of a kind 7: Four of a kind; four equal ranks within five cards 8: Straight flush; straight + flush 9: Royal flush; {Ace, King, Queen, Jack, Ten} + flush Acknowledgements This data set was taken off of the UCI Machine Learning Repository: Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:0 去赚积分?
    • 137浏览
    • 0下载
    • 0点赞
    • 收藏
    • 分享