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使用 CountVectorizer 进行计数

将文档转换为计数矩阵

使用 CountVectorizer 进行计数 是 CoddyKit 上的免费 NLP Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 NLP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 NLP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Meet CountVectorizer

The CountVectorizer from scikit-learn builds your vocabulary and counts words for you in just a few lines. 🚀

from sklearn.feature_extraction.text import CountVectorizer

Create the Vectorizer

First make an instance. With no arguments it uses sensible defaults for tokenizing and lowercasing text.

vectorizer = CountVectorizer()

Fit Learns the Vocabulary

Calling fit scans your documents and discovers every unique word, building the vocabulary automatically.

vectorizer.fit(docs)

Transform Produces Counts

Then transform turns each document into its count vector, giving you a numeric matrix of word frequencies.

X = vectorizer.transform(docs)

Fit and Transform Together

The shortcut fit_transform does both steps at once on your training text, which is the common workflow.

X = vectorizer.fit_transform(docs)

See the Vocabulary

Inspect the learned words and their indices with get_feature_names_out. These are your matrix columns.

print(vectorizer.get_feature_names_out())

Output Is a Sparse Matrix

To save memory, the result is a sparse matrix that stores only the non-zero counts, not every zero slot.

Peek at the Numbers

Convert to a dense array to actually read the counts. Use toarray for small examples only.

print(X.toarray())

Lowercasing Is Automatic

By default it lowercases text and splits on word boundaries, so The and the count as the same token.

Built-In Cleaning Options

You can pass stop_words, min_df, or max_features to prune the vocabulary right inside the vectorizer.

CountVectorizer(stop_words="english", max_features=1000)

Reuse on New Text

Never refit on test data. Call only transform so new documents use the exact same vocabulary you trained.

X_new = vectorizer.transform(new_docs)

Quick Check

Which call learns the vocabulary and counts in one step?

Recap: CountVectorizer

You used CountVectorizer to fit a vocabulary, transform text into counts, inspect features, and reuse it on new data. 🎉

常见问题解答

「使用 CountVectorizer 进行计数」课时是免费的吗？

是的 — 「使用 CountVectorizer 进行计数」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 NLP Academy 课程的其余内容，请升级到 CoddyKit PRO。 NLP Academy 课程共包含 4 节课。

「使用 CountVectorizer 进行计数」这节课中我会学到什么？

将文档转换为计数矩阵你通过在浏览器中直接运行的动手代码来练习 NLP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 NLP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 NLP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「使用 CountVectorizer 进行计数」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 NLP Academy 课中编写并运行代码吗？

能。每节 NLP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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