7 Readability Features for Your Next Machine Learning Model
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A practical guide to extracting seven readability and text-complexity features from raw text using the Textstat Python library. Covers the Flesch Reading Ease formula, Flesch-Kincaid Grade Level, SMOG Index, Gunning Fog Index, Automated Readability Index, Dale-Chall Readability Score, and the consensus Text Standard metric. Each metric is explained with its formula, behavior, and practical considerations for use as features in classification or regression machine learning models.
Table of contents
Introduction1. Applying the Flesch Reading Ease Formula2. Computing Flesch-Kincaid Grade Levels3. Computing the SMOG Index4. Calculating the Gunning Fog Index5. Calculating the Automated Readability Index6. Calculating the Dale-Chall Readability Score7. Using Text Standard as a Consensus MetricWrapping UpSort: