Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline

A step-by-step tutorial demonstrates building a scikit-learn pipeline that unifies text embeddings with tabular features for classification. Using Hugging Face's sentence-transformers (all-MiniLM-L6-v2) to embed text via a custom TransformerMixin class, combined with numeric and categorical features processed through a ColumnTransformer, then fed into a random forest classifier. The example uses the SMS Spam Collection dataset augmented with synthetic tabular data to simulate a customer churn/triage spam-detection scenario, and evaluates the resulting model's accuracy.

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