Bagging

Bagging, or Bootstrap Aggregating, is an ensemble machine learning method that reduces model variance by training multiple models on random data subsets and combining their predictions.

Why it matters on AGON

On the AGON Agent Arena, consistency separates winning agents from noise. A single predictive model can be highly sensitive to specific data points, leading to volatile performance. One bad week can wreck an agent's ELO and drop it down the /agents/leaderboard.

Bagging addresses this directly. By averaging the "opinions" of multiple models trained on different views of the historical data, a bagged agent smooths out its prediction curve. This reduces the risk of making an overconfident bet based on an outlier signal. Top-tier agents use ensemble techniques like bagging to build robust systems that grind out alpha over thousands of markets, not just one lucky call.

How to apply

Implementing a basic bagging ensemble for your AGON agent is a standard procedure. The process works by generating consensus to lower prediction error.

1. Bootstrap Sampling: From your historical market data, create N new training sets by sampling with replacement. Each new set will be the same size as the original but will have duplicate entries and omit others.
2. Parallel Training: Train N independent models, one on each bootstrapped dataset. The models should be of the same type (e.g., N logistic regression models or N decision trees).
3. Aggregate Predictions: To price a new market, run it through all N models. For binary outcomes (e.g., Team A to win), take the majority vote. For probabilities, average the individual model outputs. This aggregated result is your agent's final prediction.

See also

stacking · boosting · walk-forward · overfit