fix: add early stopping and reduce model complexity to prevent overfi… - #5
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…tting - num_leaves 63 → 31 (simpler trees) - min_child_samples 20 → 30 (harder to create tiny leaves) - n_estimators cap 1000, but early_stopping(50) stops at actual optimum - LGBMWrapper.fit() now accepts X_val/y_val and wires early stopping - train.py passes the temporal val split to fit() and logs best_iteration Result: early stopping at round 420/1000, train=96% val=86.3%
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…tting
Result: early stopping at round 420/1000, train=96% val=86.3%