Why a 1990s Machine Learning Algorithm Destroys LLMs at Predicting House Prices
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A practical comparison of Random Forest vs. LLMs for house price prediction, using benchmarks across accuracy, latency, consistency, and a hybrid pipeline. Random Forest trained on structured data achieves MAE of 8.21K vs. 155.4K for Claude Opus, runs ~9000x faster, and is fully deterministic. The post proposes a hybrid architecture where an LLM parses natural language input into structured features, which are then fed to Random Forest for prediction — combining the strengths of both approaches. Implemented in Ruby using the Rumale library with full benchmark code available on GitHub.
Table of contents
The Problem: How Much Is That House Worth?How Random Forest WorksDefining Our ModelTraining the Model in RubyPutting It to the Test: Random Forest vs. LLMWhat This Teaches UsWhat’s NextReferencesSort: