We explore how AI models generate probabilistic forecasts, quantify uncertainty, and provide interpretable insights for real-world decision-making problems.
Raw data undergoes rigorous preprocessing including normalization, handling of missing values, and feature engineering to extract meaningful signals while avoiding data leakage.
We evaluate ensemble methods (XGBoost, Random Forests) and neural architectures based on the problem domain, prioritizing models that provide native uncertainty quantification.
Cross-validation with temporal splits for time-series data ensures models generalize beyond training periods. Hyperparameter tuning balances complexity and performance.
Post-hoc calibration techniques like Platt scaling and isotonic regression align predicted probabilities with observed frequencies, ensuring reliability of confidence scores.
Historical simulation tests model performance across varying conditions. Metrics include Brier score, log loss, and calibration curves alongside domain-specific measures.
SHAP values and permutation importance reveal which factors drive predictions, enabling users to understand and critically evaluate model reasoning.
Point predictions without confidence intervals are incomplete. Real-world decisions require understanding not just what is most likely, but how confident we should be in that assessment.
Modeling match results through multi-factor analysis combining team statistics, historical performance, player metrics, and situational context. Our approach emphasizes probability distributions over point predictions.
Key challenges include handling sparse data for less-frequent events, accounting for time-varying team strength, and properly weighting recency versus sample size tradeoffs.
Frameworks for making optimal choices when outcomes are uncertain. Integrating prediction models with decision theory to balance expected value against risk tolerance.
Developing and applying techniques to explain why models make specific predictions. Focus on post-hoc explanations, counterfactual analysis, and feature attribution methods.
Mean squared error between predicted probabilities and outcomes. Rewards both calibration and discrimination.
Penalizes confident incorrect predictions more heavily. Essential for evaluating probabilistic classifiers.
Visual comparison of predicted vs. actual frequencies. Ideal models follow the diagonal.
Measures discrimination ability across thresholds. Threshold-independent ranking performance.
We document our modeling approaches, including limitations and failure modes. Predictions come with explanations of key contributing factors and confidence bounds.
Every prediction is accompanied by feature importance scores using SHAP values, showing which inputs drove the model's output and enabling users to evaluate reasoning.
We report historical performance transparently, including periods of poor calibration. No cherry-picking favorable results—full backtesting data is available.
Access detailed case studies, model documentation, and evaluation reports. We believe in open research and transparent analysis.