Text classification is a fundamental machine learning problem with applications across various products. In this guide, we have broken down the text classification workflow into several steps. For each step, we have suggested a customized approach based on the characteristics of your specific dataset. In particular, using the ratio of number of samples to the number of words per sample, we suggest a model type that gets you closer to the best performance quickly. The other steps are engineered around this choice. We hope that following the guide, the accompanying code , and the flowchart will help you learn, understand, and get a swift first-cut solution to your text classification problem.
Conclusion
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Last updated 2025-08-25 UTC.
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