Machine Learning Model Accuracy Debugging and Calibration Files

The title 'accuarcy-debug.zip' suggests our focus on code or data related to accuracy debugging. The file '222' appears to be a mere identifier. Inside the zip file are three key components: 1. `accuracy_debug.py`: This Python script likely contains code for checking and debugging model accuracy in machine learning. It typically involves loading models, predicting with test datasets, and comparing predictions to actual labels for accuracy computation. It may also analyze performance metrics like confusion matrices or ROC curves. 2. `dsgn2_calibrated_model.onnx`: An ONNX model file indicating a calibrated second-generation model designed for enhanced prediction accuracy or reduced bias. 3. `calibration_data`: Likely a dataset used for model calibration, adjusting output probability distributions to better match real-world data. Methods like Platt Scaling or isotonic regression may improve uncertainty estimation, crucial for applications like medical diagnostics or autonomous driving. These files encompass crucial aspects: model accuracy assessment, ONNX model deployment, calibration techniques, and Python's role in ML. Exploring them enhances understanding of ML model lifecycles, from training and deployment to ongoing optimization.