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组合多种特征类型

拼接文本特征与数值特征

组合多种特征类型 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。

One Type Is Rarely Enough

TF-IDF captures words, your hand-built numbers capture style. The best models often combine several feature types into one input.

Text and Numbers Together

Imagine a review's TF-IDF vector plus its length and star rating. Stacking these gives the model both word and numeric signal at once.

The Shape Problem

TF-IDF outputs a sparse matrix, while your custom features are a small dense array. You must join them along the same rows.

Side by Side, Not Stacked

We glue features as new columns for the same documents, not new rows. This horizontal join is called concatenation.

Stacking Sparse Matrices

SciPy offers hstack to place matrices side by side efficiently. It keeps everything sparse, so memory stays under control.

from scipy.sparse import hstack
combined = hstack([tfidf_matrix, numeric_features])

ColumnTransformer to the Rescue

scikit-learn's ColumnTransformer applies different steps to different columns. It is the clean way to route text and numbers through one pipeline.

Wiring It Up

You give ColumnTransformer a list of named transformers and the columns each one handles. It builds a single feature matrix for you.

from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("text", TfidfVectorizer(), "review"), ("num", "passthrough", ["length"])])

FeatureUnion for Parallel Steps

When several transformers read the same input, FeatureUnion runs them in parallel and joins the outputs. It is built for combining feature extractors.

Mind the Scales

Raw word counts and a 0-to-1000 length live on very different ranges. Mixing them often calls for scaling so no feature dominates.

Let Data Decide

Adding feature types should be a measured experiment. Compare a validation score before and after to confirm the combo actually helps.

More Is Not Always Better

Throwing in every feature can add noise and slow training. Aim for a small, well-chosen mix over a giant kitchen sink. 🧹

Quick Check

How should TF-IDF and numeric features be merged?

Recap

Strong models combine text and numeric features by concatenating columns. Use hstack, ColumnTransformer, or FeatureUnion, and scale before mixing. ✅

常见问题解答

「组合多种特征类型」课时是免费的吗？

是的 — 「组合多种特征类型」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。

「组合多种特征类型」这节课中我会学到什么？

拼接文本特征与数值特征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。

「组合多种特征类型」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。

此课程中的所有课时

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