How to Combine LLM Embeddings + TF-IDF + Metadata in One Scikit-learn Pipeline - MachineLearningMastery.com

Machine Learning Mastery

by Iván Palomares Carrascosa

February 25, 2026

AI-Generated Deep Dive Summary

This article provides a detailed guide on constructing a unified text classification pipeline using three complementary feature sources: large language model (LLM) embeddings, TF-IDF features, and structured metadata. By combining these diverse data types—dense semantic information from LLM-generated embeddings, sparse lexical features from TF-IDF, and structured metadata signals—the approach enhances the performance of downstream machine learning tasks. The article demonstrates how to integrate these components into a single Scikit-learn pipeline using tools like `ColumnTransformer` and `Pipeline`. This method allows for efficient data fusion, enabling models to leverage both fine-grained semantic understanding from embeddings and more general lexical patterns from TF-IDF. Additionally, synthetic metadata features, such as character length, word count, and uppercase/digit ratios, are generated to supplement the text data. The process involves importing necessary libraries, loading a dataset (e.g., 20 Newsgroups), and preparing the data for feature extraction. Separate pipelines are built for TF-IDF, LLM embeddings, and metadata processing. These branches are then merged using `ColumnTransformer`, creating a seamless end-to-end workflow. The article emphasizes the practicality of this approach, as it simplifies complex data fusion tasks and improves model performance by combining diverse information sources. For AI/ML practitioners, this technique is particularly valuable because it demonstrates how to effectively integrate multiple data types into a single, efficient pipeline. By leveraging both semantic and lexical features alongside structured metadata, models can gain deeper insights into text data, leading to better classification results. The use of Scikit-learn ensures compatibility with existing workflows and makes the approach accessible to a wide range of users.

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Originally published on Machine Learning Mastery on 2/25/2026