| Component | Details | |-----------|----------| | Base Encoder | Gradient‑boosted decision trees (XGBoost 1.7) with depth = 8, 500 trees. | | Multi‑Task Head | Shared encoder → three parallel softmax classifiers. | | Loss | Weighted sum of cross‑entropy losses (weights tuned to balance class frequencies). | | Regularisation | L2 penalty (λ = 0.01) + early stopping (patience = 30). | | Training/Validation Split | 70 % train, 15 % validation, 15 % hold‑out test (stratified by gender). | | Evaluation Metrics | Macro‑F1, AUROC (binary tasks), Calibration error (reliability diagrams). |

| Baseline | Description | |----------|-------------| | Random Guess | Uniform distribution over classes. | | Heuristic | Gender → nickname gender‑specific tokens; Age → province‑average age from census; Payment propensity → presence of “WeChat Pay” badge. | | Logistic Regression | Linear model on same feature set. |

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