{"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":237,"pdf_page":253,"physical_index":253,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"9207f2644f69516cfb7cd53bedc1c0d2e236f17711f32910078fc46dafa47672"},"chapter":"第8章","content":"8.3 过拟合处理\n\n我们在第5章中讲到了一个过拟合的例子。在如图8-31所示的分类问题中，我们的目标是找到一个分类器，分割两种标签的数据。用肉眼不难看出，蓝色标签的点（圆圈）集中在图的右上区域，中间的图是最为合适的分类器。左图用一条直线分割平面，模型过于简单，对直线右侧的红色标签数据（叉叉）刻画较差，属于欠拟合；而右图则用了比较复杂的模型，把样本集的数据全部照顾到，属于过拟合。\n\n虽然所有训练样本在图8-31右图上都已完全正确分类，但是这时候会有过拟合的问题。问题就是，如果加入新的样本，会怎么样？最终可能会遇到一些新样本，对于这些样本，模型无法很好地进行预测。从某种意义上讲，这个模型最初可能过拟合了训练数据，而无法很好地对之前未出现的新数据进行分类。所以，如果模型过于复杂，那么过拟合真的会成为问题。我们再来看一个例子，如图8-32所示。\n\n左图上有蓝色（集中在右上）和黄色（集中在左下）两种点。这可能代表两个类别，比如健康和不健康。中图是某一种模型，看上去误差很小，虽然这个模型有点复杂，但是在中图上有效地区分了这两类点。右图上加入了更多的测试数据，我们发现，这个模型对不少新数据的分类是错误的。对于这个模型，我们认为它过拟合了。这个过拟合模型在训练时的误差小，但是在预测新数据时误差大。\n\n第8章 TensorFlow\n\n237","previous_page":236,"next_page":238}