In order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely:
Tokenizing strings and giving an integer id for each possible token, for instance by using white-spaces and punctuation as token separators.
Counting the occurrences of tokens in each document.
normalizing and weighting with diminishing importance tokens that occur in the majority of samples / documents.
Features and samples are defined as follows:
Each individual token occurrence frequency (normalized or not) is treated as a feature. The vector of all the token frequencies for a given document is considered a multivariate sample.
在自然语言上训练机器学习分类器的一种方法是使用词包技术。Sklearn有CountVectorizer函数来执行标记化。你知道吗
根据文件:
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