{
  "fetched_at": "2026-10-24T18:05:26Z",
  "source_url": "https://www.kaggle.com/competitions/fcupractice2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Dataset information\n###欄位意義:\n- playerID:球員編號\n- yearID:年份\n- stint:球員在一季中出場的順序\n- teamID:隊伍編號\n- lgID:\b美聯(AL)或國聯(NL)\n- salary_next_year:明年合約的薪水\n- W:勝場次\n- L:敗場次\n- G:出場次數\n- GS:球員是先發投手的次數\n- CG:投手獨力投完全場的比賽次數\n- SHO:投完整場比賽，且沒有失去任何分數的次數\n- SV:救援成功次數(投手沒有搞砸球隊優勢)\n- IPouts:投球局數*3\n- H:被安打數\n- ER:自責分\n- HR:被全壘打數\n- BB:四壞保送次數\n- SO:三振次數\n- BAOpp:對手平均打擊率\n- ERA:防禦率\n- IBB:四壞球次數\n- WB:暴投次數\n- HBP:觸身球次數\n- BK:投手犯規次數\n- BFP:單一投手面臨多少打者\n- GF:完成比賽之次數(記於最後一任投手上)\n- R:打擊分數總計\n- SH:犧牲觸擊次數\n- SF:飛犧牲打次數\n- GIDP:雙殺打次數",
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    {
      "name": "Description",
      "content": "<h2>簡述</h2>\n本試題之任務，資料來自公開資料集Lahman’s Baseball Database，基於棒球選手歷次的表現，預測其年薪。\n\n<h2>進行辦法</h2>\n利用機器學習模型進行回歸預測。\n \n<h2>評量標準</h2>\n- 以test資料集分類結果的準確率,做為評比標準。\n- 合格成績需比 Leaderboard 的 baseline 有更低的Loss。\n",
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    {
      "name": "Evaluation",
      "content": "## Evaluation metric\n- 本次繳交之分數計算是mean absolute error\n<img src='https://miro.medium.com/max/780/1*8DXbECB9pnKxTpIvuVD-vg.png'>",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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