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

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.

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

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

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

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

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

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

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

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

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

How should TF-IDF and numeric features be merged?

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