This content originally appeared on DEV Community and was authored by Mehdi Imani
An interesting comprehensive review of 240 studies shows how ML & DL are reshaping churn prediction, highlighting trends, gaps, and a roadmap for future research.
🔹 ML models trends → Random Forest and Boosting lead with steady growth, while Logistic Regression and SVM remain staples. KNN and Naïve Bayes lag behind.
🔹 DL models trends → Deep Neural Networks dominate. CNNs, RNNs, LSTMs, and even Transformers appear, but at smaller scales.
👉 Together, the field still leans heavily on tree-based ML, while DL is emerging for richer and sequential data.
Full open-access review: https://www.mdpi.com/3508932
💬 What’s your experience — do RF/XGBoost still win in production churn tasks, or are DL approaches catching up?
This content originally appeared on DEV Community and was authored by Mehdi Imani
Mehdi Imani | Sciencx (2025-09-26T20:35:38+00:00) Which models dominate churn prediction? Insights from 240 ML/DL studies (2020–2024). Retrieved from https://www.scien.cx/2025/09/26/which-models-dominate-churn-prediction-insights-from-240-ml-dl-studies-2020-2024/
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