Tuning a Churn-Prediction Random Forest with GASearchCV

说明与用途

GASearchCV 将流失预测模型的召回率从48%提升至83%，F1得分从0.543提升至0.604。

问题

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

结果

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.

工作流

• 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.

证据

Recall (Churn) 0.481 (48%)

来源

Recall (Churn) 0.826 (83%)

来源

scoring='recall'

来源