NLP Academy · 课时

使用 TF-IDF 特征进行训练

在 scikit-learn 中训练分类器

使用 TF-IDF 特征进行训练 是 CoddyKit 上的免费 NLP Academy 课时。这是第 2 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。

From Text to Numbers First

A model cannot read raw sentences. You first convert documents into TF-IDF vectors, then train logistic regression on those numbers. 🔢

Fit the Vectorizer

The TfidfVectorizer learns your vocabulary and weighting from the training texts. One call turns a list of strings into a feature matrix.

from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer()
X = vec.fit_transform(train_texts)

Labels Stay Separate

Your features X come from the text, but your labels y come from you. Each document needs its correct class, like spam or not-spam.

Split Before You Train

Hold out a test set so you can judge fairly. train_test_split keeps some data unseen until the very end of evaluation.

from sklearn.model_selection import train_test_split
X_tr, X_te, y_tr, y_te = train_test_split(X, y)

Fit Means Learn

Calling fit tells logistic regression to find the word weights that best separate your classes on the training data.

from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(max_iter=1000)
clf.fit(X_tr, y_tr)

Raise max_iter If It Warns

Text has many features, so the solver may need more steps. Bumping max_iter clears the common convergence warning you will see.

Predict on New Vectors

To classify fresh text, transform it with the same fitted vectorizer, then call predict. Never refit the vectorizer on test data.

preds = clf.predict(X_te)

Transform, Do Not Fit, on Test

Test text uses transform, not fit_transform. Refitting would leak test information and quietly inflate your scores.

Check the Accuracy

A quick score call gives accuracy on the held-out set. It is your first signal that training actually worked.

print(clf.score(X_te, y_te))

Same Steps Stay in Sync

Train and prediction must share the exact same vocabulary. Using one fitted vectorizer everywhere keeps feature columns aligned.

A Pipeline Saves You

Wrapping the vectorizer and model in a Pipeline chains transform and predict automatically, so you never forget a step.

from sklearn.pipeline import make_pipeline
pipe = make_pipeline(TfidfVectorizer(), LogisticRegression())

Quick Check

How should you prepare test text before predicting?

Recap: Vectorize Then Fit

Vectorize text with TF-IDF, split, then fit logistic regression. Reuse the same vectorizer for test data, ideally inside a pipeline. ✅

常见问题解答

「使用 TF-IDF 特征进行训练」课时是免费的吗？

是的 — 「使用 TF-IDF 特征进行训练」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 TF-IDF 特征进行训练」这节课中我会学到什么？

在 scikit-learn 中训练分类器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 2 节课，共 4 节。

「使用 TF-IDF 特征进行训练」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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