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Code correctness: - Fix Layer/MLP output unwrapping in autodiff to prevent TypeError - Use weighted degree in graph Laplacian, handle dangling nodes in PageRank - Fix isolation forest c(n) base case and score normalization - Fix test label leakage in feature selection and threshold tuning - Remove MSE censoring that biased learning curve averages - Handle edge case of single minority sample in SMOTE - Build random baseline from training labels, not test labels - Re-normalize stationary distribution after clipping numerical errors - Add input validation (spectral clustering, MH sampler, NB counts, gamma shape, forecast history, convolution lengths) Documentation: - Add __radd__ to autodiff docs for reflected arithmetic - Correct GCN formula to include self-loops (A_hat = A + I) - Soften NB probability calibration claim - Fix Optuna pruning example to use trial.report/should_prune - Tune threshold on validation data not test data in docs - Qualify Bayesian early stopping claim - Fix comparison snippet in hyperparameter tuning docs - Add language IDs to fenced code blocks (MD040) - Escape pipe in table cell, fix step references - Soften absolute metric claims, add target encoding leakage note - Use endpoint-free time grid for FFT alignment Style: - Remove unnecessary f-string prefixes across 4 files