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For statistical learning, categorical variables in a table are usuallyconsidered as discrete entities and encoded separately to feature vectors,e.g., with one-hot encoding. "Dirty" non-curated data gives rise to categoricalvariables with a very high cardinality but redundancy: several categoriesreflect the same entity. In databases, this issue is typically solved with adeduplication step. We show that a simple approach that exposes the redundancyto the learning algorithm brings significant gains. We study a generalizationof one-hot encoding, similarity encoding, that builds feature vectors fromsimilarities across categories. We perform a thorough empirical validation onnon-curated tables, a problem seldom studied in machine learning. Results onseven real-world datasets show that similarity encoding brings significantgains in prediction in comparison with known encoding methods for categories orstrings, notably one-hot encoding and bag of character n-grams. We drawpractical recommendations for encoding dirty categories: 3-gram similarityappears to be a good choice to capture morphological resemblance. For veryhigh-cardinality, dimensionality reduction significantly reduces thecomputational cost with little loss in performance: random projections orchoosing a subset of prototype categories still outperforms classic encodingapproaches.

Similarity encoding for learning with dirty categorical variables