{"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":184,"pdf_page":200,"physical_index":200,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"9a980adf46dac1b3bafcaa6fdcb51ff2464cfa2cda7e2047dd8637466612476c"},"chapter":"第6章","content":"6.6.3 集成算法之 Boosting\n\n6.6.2小节讲解了Bagging及其代表算法随机森林。本节我们来介绍集成算法的另一大思想：Boosting。前文提到，Boosting和Bagging最大的区别是Boosting的一轮轮训练是依次进行的，而不是像Bagging一样是相互独立的。其核心过程可以用图6-28来描述。\n\nBoosting的核心策略是一步步修正模型，使之逐渐逼近真实的映射关系。每一个弱分类器是建立在前一个的基础上进行的，受到前一个分类器效果的反馈加以改进，使得当前的分类器比前一轮做得更好。Boosting的代表算法有Adaboost、GBDT和Xgboost。后两者和RandomForest是机器学习竞赛中效果最出色的模型，在工业界中也极其受欢迎。\n\nAdaboost\n\nAdaboost主要用于分类，其核心思路是在每一个弱分类器之后对预测值和真实值进行比较，然后将分类错误的样本点在下一个分类器中赋予更高的权重，使得在下一次训练中不容易再被分错。其算法如下：\n\n人工智能与大数据技术导论\n\nEnd\n\n184","previous_page":183,"next_page":185}