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Predict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies
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基于 SOC 职业分类
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| name | tabular-chained-target-prediction |
| description | Predict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies |
When multiple targets are correlated (e.g., different engagement metrics for the same entity), predicting them independently wastes inter-target signal. Chained prediction trains target1 first, then feeds its prediction as an additional feature when training target2, and so on. This captures causal or correlational structure between targets without requiring a multi-output model.
import lightgbm as lgb
import numpy as np
targets = ["target1", "target2", "target3", "target4"]
models = {}
feature_cols_base = [c for c in X.columns if c not in targets]
for i, target in enumerate(targets):
feat_cols = feature_cols_base + targets[:i] # add earlier targets as features
model = lgb.LGBMRegressor(**params[target])
model.fit(X_train[feat_cols], y_train[target])
models[target] = model
# Add predictions to both train and test for downstream targets
X_train[target] = np.clip(model.predict(X_train[feat_cols]), 0, 100)
X_test[target] = np.clip(model.predict(X_test[feat_cols]), 0, 100)