{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":148,"pdf_page":164,"physical_index":164,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"203a1d935205eba0a38675a09aeb6acbe72b11432a3e06c376a949d3dd39c52c"},"chapter":"第5章","content":"模型2：y= 2x\n\n需要指出的是，（1）公式在统计学上不是严谨的写法，但我们在讨论机器学习时可以这样简写。y与x在统计学意义上的关系可以由下式给出：\n\n$$\n\\mathbf { y } = \\mathbf { a } 0 + \\mathbf { a } 1 \\mathbf { x } + \\mathbf { e p s i l o n }\n$$\n\n或\n\n$$\n\\operatorname { E } ( \\mathbf { y } | \\mathbf { x } ) = \\mathbf { \\mu } _ { \\mathrm { a 0 } } + \\mathbf { a } \\mathbf { l } \\mathbf { x }\n$$\n\n第一个式子中的epsilon是误差项，通常服从标准正态分布。第二个式子的含义与第一个相同，y在给定x的情况下服从条件正态分布，并且条件期望是a0+a1x。\n\n在确定参数a0、a1之前，可以把上面的式子看成一系列模型，或者称为模型簇(setofmodels)。这些参数一旦取定之后，这个式子就成为一个确定模型。比如y=4+x和y =2x就是两个具体的模型，相对应的参数（a0,a1）分别为（4,0）和（0,2）。\n\n5.2 误差和 MSE\n\n假设我们有一组带标签的样本(x,y)，如表5-2所示。\n\n让我们尝试用下面两个模型对原样本数据进行预测：\n\n模型1: y = x + 4\n\n第一个模型的预测效果如表5-3所示。\n\n第二个模型的预测效果如表5-4所示。\n\n上面两个表中的误差是预测值与真实值之间的距离，描述了预测值与真实值之间的偏离程度。为了评价模型的拟合效果，我们需要计算均方误差(MeanSquaredError，MSE）。均方\n\n人工智能与大数据技术导论\n\n148","previous_page":147,"next_page":149}