X @Avi Chawla

Model Calibration Importance - Modern neural networks can be misleading due to overconfidence in predictions [1][2] - Calibration ensures predicted probabilities align with actual outcomes, crucial for reliable decision-making [2][3] - Overly confident but inaccurate models can lead to suboptimal decisions, exemplified by unnecessary medical tests [3] Calibration Assessment - Reliability Diagrams visually inspect model calibration by plotting expected accuracy against confidence [4] - Expected Calibration Error (ECE) quantifies miscalibration, approximated by averaging accuracy/confidence differences across bins [6] Calibration Techniques - Calibration is important when probabilities matter and models are operationally similar [7] - Binary classification models can be calibrated using histogram binning, isotonic regression, or Platt scaling [7] - Multiclass classification models can be calibrated using binning methods or matrix and vector scaling [7] Experimental Results - LeNet model achieved an accuracy of approximately 55% with an average confidence of approximately 54% [5] - ResNet model achieved an accuracy of approximately 70% but with a higher average confidence of approximately 90%, indicating overconfidence [5] - ResNet model thinks it's 90% confident in its predictions, in reality, it only turns out to be 70% accurate [2]