{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","uuid":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","normalized_title":"智慧的疆界从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","isbns":["9787111778004","9787111695714"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":456,"navigation_url":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title_url":"https://workshop.closeclaw.top/ai/title/%E6%99%BA%E6%85%A7%E7%9A%84%E7%96%86%E7%95%8C%20%E4%BB%8E%E5%9B%BE%E7%81%B5%E6%9C%BA%E5%88%B0%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","page_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/search"},"page":{"printed_page":208,"pdf_page":223,"physical_index":223,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"2a328449c6a7d98a0d3b8220c345e3a24af4e0e7377f00b3b2b54b5e32e261c9"},"chapter":"内容简介","content":"这里举个直观的例子来帮助大家理解，用射击比赛来类比偏差和方差对结果的影响。假设射击运动员在 10 环靶中只打到了 7 环，产生的 3 环的差距就是期望目标与实际目标的差距，也就是误差，这个误差产生的原因可能是他瞄准的时候没瞄好，本来就是朝着 7 环去打的，也可能是他瞄准的确实是 10 环靶心，但是手不够稳定，打到了 7 环上。这里“瞄不准，手很稳”的情况就相当于偏差大、方差小所构成的误差，而“瞄得准，手不稳”的情况就相当于偏差小、方差大所构成的误差。在这个例子中，偏差和方差对结果的影响，可以通过图 6-20 直观地看出来。\n\n由偏差和方差的含义，我们可以感觉到它们本身是有潜在冲突的。给定一个学习任务，假设我们能够通过不同的训练程度来控制模型的复杂程度，在模型过于简单时，它的拟合能力不够强，训练数据的变化不足以使模型结果产生显著变化，无法通过样本的特征变化得到正确的学习结果，此时偏差主导了期望泛化误差，机器学习中将这种情况称为“欠拟合”（Underfitting），在某些资\n\n208\n\n第三部分 第三波高潮","previous_page":207,"next_page":209}