你聽過CatBoost嗎?本文教你如何使用CatBoost進行快速梯度提升

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ML進行設備上推理(iOS)。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"可以在內部處理缺失值。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"可用於迴歸和分類問題。"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" "}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"訓練參數"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" 讓我們看一下CatBoost中的常用參數:"}]},{"type":"bulletedlist","content":[{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"loss_function"}]},{"type":"text","text":" 別名爲 "},{"type":"codeinline","content":[{"type":"text","text":"objective"}]},{"type":"text","text":" -用於訓練的指標。這些是迴歸指標,例如用於迴歸的均方根誤差和用於分類的對數損失。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"eval_metric"}]},{"type":"text","text":" —用於檢測過度擬合的度量。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"iterations"}]},{"type":"text","text":" -待建的樹的最大數量,默認爲1000。別名是 "},{"type":"codeinline","content":[{"type":"text","text":"num_boost_round"}]},{"type":"text","text":", "},{"type":"codeinline","content":[{"type":"text","text":"n_estimators"}]},{"type":"text","text":"和 "},{"type":"codeinline","content":[{"type":"text","text":"num_trees"}]},{"type":"text","text":"。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"learning_rate"}]},{"type":"text","text":" 別名 "},{"type":"codeinline","content":[{"type":"text","text":"eta"}]},{"type":"text","text":" -學習速率,確定模型將學習多快或多慢。默認值通常爲0.03。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"random_seed"}]},{"type":"text","text":" 別名 "},{"type":"codeinline","content":[{"type":"text","text":"random_state"}]},{"type":"text","text":" —用於訓練的隨機種子。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"l2_leaf_reg"}]},{"type":"text","text":" 別名 "},{"type":"codeinline","content":[{"type":"text","text":"reg_lambda"}]},{"type":"text","text":" —成本函數的L2正則化項的係數。默認值爲3.0。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"bootstrap_type"}]},{"type":"text","text":" —確定對象權重的採樣方法,例如貝葉斯,貝努利,MVS和泊松。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"depth"}]},{"type":"text","text":" —樹的深度。"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"grow_policy"}]},{"type":"text","text":" —確定如何應用貪婪搜索算法。它可以是 "},{"type":"codeinline","content":[{"type":"text","text":"SymmetricTree"}]},{"type":"text","text":", "},{"type":"codeinline","content":[{"type":"text","text":"Depthwise"}]},{"type":"text","text":"或 "},{"type":"codeinline","content":[{"type":"text","text":"Lossguide"}]},{"type":"text","text":"。 "},{"type":"codeinline","content":[{"type":"text","text":"SymmetricTree"}]},{"type":"text","text":" 是默認值。在中 "},{"type":"codeinline","content":[{"type":"text","text":"SymmetricTree"}]},{"type":"text","text":",逐級構建樹,直到達到深度爲止。在每個步驟中,以相同條件分割前一棵樹的葉子。當 "},{"type":"codeinline","content":[{"type":"text","text":"Depthwise"}]},{"type":"text","text":" 被選擇,一棵樹是內置一步步驟,直到指定的深度實現。在每個步驟中,將最後一棵樹級別的所有非終端葉子分開。使用導致最佳損失改善的條件來分裂葉子。在中 "},{"type":"codeinline","content":[{"type":"text","text":"Lossguide"}]},{"type":"text","text":",逐葉構建樹,直到達到指定的葉數。在每個步驟中,將損耗改善最佳的非終端葉子進行拆分"}]}]},{"type":"listitem","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"codeinline","content":[{"type":"text","text":"min_data_in_leaf"}]},{"type":"text","text":" 別名 "},{"type":"codeinline","content":[{"type":"text","text":"min_child_samples"}]},{"type":"text","text":" —這是一片葉子中訓練樣本的最小數量。此參數僅與 "},{"type":"codeinline","content":[{"type":"text","text":"Lossguide"}]},{"type":"text","text":" 和 "},{"type":"codeinline","content":[{"type":"text","text":"Depthwise"}]},{"type":"te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0.5。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/17/17ddb48738f3f83d61a6bb41d9cf9b63.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/20/2011a911cca3feeae22f2952ee70911c.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"CatBoost還爲我們提供了包含所有模型參數的字典。我們可以通過遍歷字典來打印它們。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/e0/e0c33f809a7c5821d8c568ebd23bf66f.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/3e/3e6b9d77905bcc237a429bb60039b465.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"結尾"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在本文中,我們探討了CatBoost的優點和侷限性以及主要的訓練參數。然後,我們使用scikit-learn完成了一個簡單的迴歸實現。希望這可以爲您提供有關庫的足夠信息,以便您可以進一步探索它。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"往期精彩鏈接:"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"link","attrs":{"href":"http://mp.weixin.qq.com/s?__biz=Mzg3MTM1MDI5NA==&mid=2247484181&idx=1&sn=5aaa298c83e235c9201bc58b1ba749c1&chksm=cefeaa6cf989237af75b7bff0a38fbff9b88277c14e8ccf2642a17d5074b72dcd82d3b0ea2bc#rd","title":null},"content":[{"type":"text","text":"《統計學習基礎:數據挖掘、推理和預測》-斯坦福大學人工智能學科專用教材"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/90/903237ffd0a3b3ae06272386f26ecb9e.png","alt":null,"title":null,"style":null,"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}
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