August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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机器学习
｜
149
➋
低
容量神经网络的交叉验证。
➌
高
容量神经网络的包装类。
➍
高
容量神经网络的交叉验证。
避免过拟合
一般而言，
过拟合
，即模型在训练数据集上比在验证数据集和测试数据集上
表现得更好，在机器学习中是要避免的，尤其是在金融中。适当的评估程序
和分析（比如交叉验证）有助于防止过拟合并能找到足够的模型容量。
5.8
结论
本章介绍了机器学习过程的蓝图，主要内容如下。
学习
机器学习到底意味着什么？
数据
使用哪些原始数据以及哪些（预处理）特征和标签数据？
成功
考虑到依据数据来间接定义的问题（估计、分类等），什么是衡量成功的适当标准？
容量
模型容量扮演着什么角色？基于当前的问题，多大的容量才算足够？
评估
根据所训练模型的目标，如何评估模型的性能？
偏差和方差
哪种模型更适合解决当前的问题：是有相当大偏差的模型还是有相当大方差的模型？
交叉验证
对非序列数据集来说，当对训练数据子集和验证数据子集的不同配置进行交叉验证时，
模型如何执行？
该蓝图在后续章节中会被应用到许多现实世界的金融用例中。有关机器学习过程的更多背
景信息和细节，请参阅本书参考文献。
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.