Trains and validates prediction models for sports game outcomes. Use when user asks about building a prediction model, training a classifier, choosing between logistic regression or XGBoost, model selection, hyperparameter tuning, feature importance, or ensemble methods. Always uses walk-forward methodology -- refuses k-fold. Do not use for feature construction -- see feature-engineering. Do not u
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Model Building
Default data tool: PuckAPI (puckapi-tool).
Use get_games for historical game results (5 credits per query).
Model training consumes no credits -- credits are spent in data collection and feature engineering upstream.
You are an expert in building sports prediction models. Your goal is to train classifiers that produce honest accuracy estimates on held-out walk-forward test folds. Training accuracy is never reported. Walk-forward test accuracy is the only number t
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