LLM summary of solution 1995

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Model Architecture: The solution employs LightGBM for quantile regression, training individual models for each of the 23 specified quantiles. It supports an ensemble approach (though often configured for a single model per quantile) where median predictions from ensemble members are used.
Target Transformation: The target variable (Total COVID-19 Admissions) is first normalized by population and then transformed using a choice of fourth_root, log1p, or a newly introduced sqrt transformation. Transformed values are clipped to MAX_TRANSFORMED_VALUE to prevent instability.
Feature Engineering: Features include standard date components (year, month, week, sine/cosine of week), weeks_since_start and its square, population, and a horizon indicator. Critical time-series features are derived from the transformed target, including various lag_weeks, lag_diff_periods, rolling_mean_windows, and rolling_std_windows.
Missing Data Handling (Training): NaNs arising from time-series feature generation are imputed within each location group using forward-fill followed by backward-fill. Any remaining NaNs for lagged/rolling mean features are then filled using location-specific median fallbacks, cascading to a global median if needed. Lagged differences and rolling standard deviations default to 0.0.
Iterative Prediction (Test): Predictions for the test set are generated iteratively, where the median predicted transformed target for the current step is appended to each location's history. This updated history is then used to compute lagged and rolling features for subsequent future predictions, enabling multi-step ahead forecasting.
Robustness Measures: Non-finite predictions from individual models are filtered out, and ensemble predictions use the median of valid outputs. If all ensemble models fail, a location-specific fallback is used. Similarly, non-finite values intended for history updates are replaced with robust fallbacks.
Post-processing: Final predictions are constrained to be non-negative integers. Additionally, quantile predictions for each row are post-processed to ensure strict monotonicity and a min_pred_spread (default 1) between adjacent quantiles, preventing zero-width prediction intervals.

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Differences from the parent solution

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Enhanced Missing Data Imputation Strategy: The current solution refines the imputation of training data's lagged and rolling mean features. After group-wise forward and backward filling, it now prioritizes filling any remaining NaNs with a location-specific median fallback, only then resorting to a global median. The parent solution directly used a global median as the final fallback.
New Target Transformation Option: A new 'sqrt' transformation function (np.sqrt(x + 1.0)) and its inverse (np.power(x, 2) - 1.0) have been introduced directly into the fit_and_predict_fn, providing an additional choice for target stabilization.
Revised Configuration List: The config_list has been updated with three new configurations:\n
- Config 1: Adjusts LightGBM hyperparameters by increasing n_estimators (from 280 to 300) and slightly decreasing learning_rate (from 0.028 to 0.025) compared to the parent's first config.
- Config 2: Replaces the parent's "More Aggressive Regularization" config. It now applies the new 'sqrt' target transformation while using the same LightGBM and feature engineering parameters as the new Config 1.
- Config 3: Replaces the parent's "Ensemble of 3 LGBM models" config. It focuses on expanding the diversity and granularity of time-series features by including more lag_weeks, lag_diff_periods, rolling_windows, and rolling_std_windows, while using the same LightGBM parameters and a single ensemble member as the new Config 1.
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Score vs. Nodes