Tuning a Churn-Prediction Random Forest with GASearchCV

Описание и применение

Генетический алгоритм GASearchCV значительно повысил показатель recall модели предсказания оттока клиентов, увеличив его с 48% до 83%, при этом F1‑score вырос с 0.543 до 0.604.

Задача

Improve recall of a churn‑prediction model for a phone company where missing a churner is costlier than a false alarm.

Результат

Recall increased from 48% to 83% (≈+35 percentage points), precision dropped to 48%, but overall F1 improved from 0.543 to 0.604, demonstrating a net performance gain by optimizing for recall.

Рабочий процесс

• Define hyperparameter search space with Integer, Continuous, and Categorical ranges.
• Instantiate GASearchCV with 5‑fold CV, recall scoring, population_size=20, generations=15, n_jobs=-1.
• Fit GASearchCV on training data.
• Retrieve best hyperparameters and evaluate on test set.
• Compare recall, precision, and F1 to baseline.

Доказательства

Recall (Churn) 0.481 (48%)

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Recall (Churn) 0.826 (83%)

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scoring='recall'

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