The csa_prediction_engine library provides a suite of tools and functions for performing relevance-based predictions using the Cambridge Sports Analytics Prediction Engine API. The package is designed to facilitate single and multi-task predictions, allowing for flexible model evaluation and experimentation.
Project description
Cambridge Sports Analytics Prediction Engine
Welcome to the CSA Prediction Engine package. This Python library provides a suite of tools and functions for performing relevance-based predictions using the Cambridge Sports Analytics (CSA) API. The package is designed to facilitate single and multi-task predictions, allowing for flexible model evaluation and experimentation.
Key Features
- Single Task Predictions: Support for predictions with one dependent variable and one set of circumstances.
- Multi-y Predictions: Perform predictions with multiple dependent variables and a single set of circumstances.
- Multi-theta Predictions: Perform predictions with one dependent variable and multiple sets of circumstances.
- Relevance-Based Grid Predictions: Generate optimal predictions by evaluating all thresholds and variable combinations.
- Grid Singularity Predictions: Analyze grid predictions to find the singular optimal solution.
- MaxFit Predictions: Find the best-fit model based on adjusted relevance.
Contributing
We welcome contributions to the CSA Relevance Engine package. If you find a bug or have a feature request, please reach out to the CSA support team: support@csanalytics.io
License
(c) 2023 - 2024 Cambridge Sports Analytics, LLC. All rights reserved.
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