Introduction

Text classification becomes harder when the label space is large, because the model must separate many classes that may have little training data each. In scikit-learn, the goal is not to find one magical classifier, but to build a strong pipeline, keep the labels clean, and choose evaluation metrics that reflect the many-class setting.

Start with a Strong Baseline Pipeline

For classical text classification, a TF-IDF representation plus a linear classifier is still a strong baseline. It is fast, interpretable, and often surprisingly competitive when you have lots of sparse features and many possible labels.

1from sklearn.feature_extraction.text import TfidfVectorizer
2from sklearn.linear_model import LogisticRegression
3from sklearn.pipeline import Pipeline
4
5model = Pipeline([
6    ("tfidf", TfidfVectorizer(ngram_range=(1, 2), min_df=2)),
7    ("clf", LogisticRegression(max_iter=2000, n_jobs=None))
8])

You can swap LogisticRegression for LinearSVC depending on whether calibrated probabilities matter. Linear models generally handle high-dimensional text features better than tree-based models in this setting.

Data Quality Usually Matters More Than the Estimator

When there are many classes, label inconsistency can destroy accuracy. Examples include near-duplicate labels, spelling variants, labels that differ only in capitalization, and classes with only a handful of examples.

Before tuning algorithms, inspect:

• how many samples each label has
• whether labels overlap semantically
• whether some classes should be merged or hierarchically organized
• whether the same text appears under conflicting labels

A classifier cannot learn stable boundaries if the label system itself is noisy.

Train and Evaluate with Stratification

Use a train-test split that preserves label distribution as much as possible.

1from sklearn.model_selection import train_test_split
2from sklearn.metrics import classification_report
3
4X_train, X_test, y_train, y_test = train_test_split(
5    texts,
6    labels,
7    test_size=0.2,
8    random_state=42,
9    stratify=labels,
10)
11
12model.fit(X_train, y_train)
13pred = model.predict(X_test)
14print(classification_report(y_test, pred))

For many-class problems, overall accuracy can be misleading if common labels dominate. Macro-averaged precision, recall, and F1 give a better view of whether minority classes are being learned at all.

Handle Rare Classes Deliberately

If many classes have very few examples, accuracy will plateau no matter how you tune the classifier. You have several options:

• gather more examples for rare labels
• merge labels that are too fine-grained for the data volume
• use class_weight="balanced" where supported
• predict a shortlist of top candidates rather than one label

1from sklearn.svm import LinearSVC
2from sklearn.pipeline import Pipeline
3
4model = Pipeline([
5    ("tfidf", TfidfVectorizer(ngram_range=(1, 2), min_df=2)),
6    ("clf", LinearSVC(class_weight="balanced"))
7])

Balanced class weights help when the label distribution is skewed, but they do not replace missing data.

Feature Engineering Still Matters

For many labels, simple bag-of-words features may miss short phrases or domain-specific wording. Useful improvements include:

• adding bigrams or trigrams
• normalizing case and punctuation consistently
• keeping discriminative tokens that a generic stop-word list might remove
• using character n-grams for noisy or misspelled text

1vectorizer = TfidfVectorizer(
2    analyzer="char_wb",
3    ngram_range=(3, 5),
4    min_df=2,
5)

Character n-grams can be strong when classes differ by product codes, abbreviations, or slight wording variations.

Consider Problem Reformulation

If the label space is extremely large, a flat classifier may not be the right interface. Sometimes the better design is:

• hierarchical classification, such as category then subcategory
• retrieval plus reranking
• top-k suggestion instead of forced single-label prediction

Scikit-learn can support parts of that workflow, but accurate results may depend more on problem framing than on another round of estimator tuning.

Common Pitfalls

• Tuning models aggressively before cleaning and consolidating the label set.
• Judging performance only by overall accuracy in a highly imbalanced many-class problem.
• Expecting rare classes to classify well when they have almost no training samples.
• Using text features that are too weak, such as unigrams only, for a nuanced label space.
• Treating a very large flat label space as mandatory when hierarchical or top-k prediction would better fit the product requirement.

Summary

• Begin with a TF-IDF plus linear-model pipeline as a strong scikit-learn baseline.
• Clean the labels and inspect class distribution before spending time on model tuning.
• Evaluate with macro metrics, not just raw accuracy.
• Handle rare classes explicitly through data collection, merging, weighting, or reformulation.
• For very large label spaces, consider hierarchical or top-k approaches instead of a flat single-label classifier.