10 Ways to Use Embeddings for Tabular ML Tasks

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Embeddings, traditionally used in NLP, can enhance tabular machine learning workflows in multiple ways. Ten strategies are presented: encoding high-cardinality categorical features, averaging word embeddings for text columns, clustering embeddings into meta-features, learning self-supervised tabular embeddings through masked

#machine-learning

#nlp

#neural-networks

#embeddings

#feature-engineering

Jan 12•5m read time•From machinelearningmastery.com

Table of contents

Introduction 1. Encoding Categorical Features With Embeddings 2. Averaging Word Embeddings for Text Columns 3. Clustering Embeddings Into Meta-Features 4. Learning Self-Supervised Tabular Embeddings 5. Building Multi-Labeled Categorical Embeddings 6. Using Contextual Embeddings for Categorical Features 7. Learning Embeddings on Binned Numerical Features 8. Fusing Embeddings and Raw Features (Interaction Features)9. Using Sentence Embeddings for Long Text 10. Feeding Embeddings Into Tree Models Closing Remarks

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