python中具有多种功能类型的机器学习 [英] Machine learning with multiple feature types in python

问题描述

我能够使用Python中的scikit-learn和NLTK模块进行一些简单的机器学习.但是在使用具有不同值类型(数字，字符串列表，是/否等)的多个功能进行训练时，我遇到了问题.在以下数据中，我有一个单词/短语列，在其中提取信息并创建相关列(例如，length列是'word/phrase'的字符长度).标签栏就是标签.

I am able to do some simple machine learning using scikit-learn and NLTK modules in Python. But I have problems when it comes to training with multiple features that have different value types (number, list of string, yes/no, etc). In the following data, I have a word/phrase column in which I extract the information and create relevant columns (for example, the length column is the character lengths of 'word/phrase'). Label column is the label.

Word/phrase Length  '2-letter substring'    'First letter'  'With space?'       Label
take action 10  ['ta', 'ak', 'ke', 'ac', 'ct', 'ti', 'io', 'on']    t   Yes     A
sure    4   ['su', 'ur', 're']  s   No      A
That wasn't     10  ['th', 'ha', 'at', 'wa', 'as', 'sn', 'nt']  t   Yes     B
simply  6   ['si', 'im', 'mp', 'pl', 'ly']  s   No      C
a lot of    6   ['lo', 'ot', 'of']  a   Yes     D
said    4   ['sa', 'ai', 'id']  s   No      B

我应该将它们合并为一个词典，然后使用sklearn的DictVectorizer将其保存在工作存储器中吗?然后在训练ML算法时将这些特征视为一个X向量?

Should I make them into one dictionary and then use sklearn's DictVectorizer to hold them in a working memory? And then treat these features as one X vector when training the ML algorithms?

推荐答案

大多数机器学习算法都使用数字，因此您可以将分类值和字符串转换为数字.

Majority of machine learning algorithms work with numbers, so you can to transform your categorical values and string into numbers.

受欢迎的python机器学习库scikit-learn的整章专门介绍数据预处理.使用是/否"，一切都很容易-只需输入0/1即可.

Popular python machine-learning library scikit-learn has the whole chapter dedicated to preprocessing of the data. With 'yes/no' everything is easy - just put 0/1 instead of it.

在许多其他重要内容中，它解释了分类的过程数据预处理，使用其 OneHotEncoder .

Among many other important things it explains the process of categorical data preprocessing using their OneHotEncoder.

在处理文本时，还必须以适当的方式转换数据.文本的一种常见特征提取策略是 tf-idf 得分，我在这里写了教程.

When you work with text, you also have to transform your data in a suitable way. One of the common feature extraction strategy for text is a tf-idf score, and I wrote a tutorial here.

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