August 2017
Intermediate to advanced
274 pages
7h 44m
Chinese
Content preview from Python数据分析基础
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232
｜
第
9
章
幕上打印出保留
3
位小数的模型准确度。
这个示例演示了在
Scikit-Learn
中使用随机森林分类器进行交叉验证的方法。除了本节中
介绍的随机森林模型，
Scikit-Learn
中还有很多其他类型的回归和分类模型。例如，只要将
下面的
import
语句添加到脚本中，并且将分类器从
clf = RF()
修改为
clf = SVC()
，就可
以实现一个支持向量机模型：
from
sklearn.svm
import
SVC
除了各种模型，
Scikit-Learn
中还有很多用于数据预处理、数据降维和模型选择的函数。
要了解更多关于
Scikit-Learn
的信息，以及其他使用交叉验证估计模型的方法，请参考
Scikit-Learn
文档（
http://scikit-learn.org/stable/index.html
）。
9.2.4
更多的扩展包
除了
NumPy
、
SciPy
和
Scikit-Learn
，根据不同类型数据分析工作的要求，你可能需要了解
更多的扩展包。下面的列表相对于
Python
包索引中成千上万的扩展包来说，只是沧海一
粟，列出的这些扩展包仅是作为一个推荐，希望你对此感兴趣并能实际应用：
• xarray
（
http://xray.readthedocs.org/en/stable/#
）
提供一个类似
pandas
的工具箱，用于多维数组分析
• SKLL
（
https://skll.readthedocs.org/en/latest/index.html
）
为一般的
Scikit-Learn
操作提供命令行工具 ...
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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.