model-building

Name: model-building
Rating: 5 (2 reviews)
Author: PuckAPI

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

2stars

Updated last month

View on GitHub ↗License: MIT

How to add

/plugin marketplace add PuckAPI/claude-sports-analytics

The exact command may vary by repository. Check the README on GitHub.

For the skill author

Drop this on your repo README

Shows your skill is listed on Skillteca, generates a backlink and trackable traffic.

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#mcp #ai #api

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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

[Description truncada. Veja o README completo no GitHub.]

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