Platt Scaling
A method proposed by John Platt in 1999 for converting the raw discriminant output of an SVM into probability estimates. It works by fitting a sigmoid function via internal cross-validation, which introduces additional training overhead.
Core Facts
Neural networks have an overconfidence defect: even when inputs fall outside the training distribution, the Softmax layer may still output inflated confidence scores.
Commonly used calibration methods include Platt Scaling (fitting a sigmoid function using logistic regression) and Isotonic Regression (fitting a non-parametric monotonic mapping using isotonic regression).
Probability calibration refers to a post-processing technique that converts model output prediction scores into true probability estimates, with commonly used methods including Platt Scaling and Isotonic Regression.
Timeline (last 90 days)
Post-processing methods such as Temperature Scaling and Platt Scaling can re-adjust the output probability distribution without changing the model's prediction ranking.
Common post-calibration techniques include Temperature Scaling, Platt Scaling, and Isotonic Regression, which remap raw outputs to more reliable probability estimates by fitting correction functions on an independent validation set without retraining the model.
Confidence calibration methods include post-processing techniques such as Platt Scaling and Temperature Scaling.
Technical approaches to addressing poor model calibration include post-processing calibration methods such as temperature scaling and Platt Scaling, as well as uncertainty estimation techniques such as Bayesian neural networks and MC Dropout.
Commonly used calibration methods include Platt Scaling (fitting a sigmoid function using logistic regression) and Isotonic Regression (fitting a non-parametric monotonic mapping using isotonic regression).
Probability calibration refers to a post-processing technique that converts model output prediction scores into true probability estimates, with commonly used methods including Platt Scaling and Isotonic Regression.
All Facts (7)
Neural networks have an overconfidence defect: even when inputs fall outside the training distribution, the Softmax layer may still output inflated confidence scores.
75%VerifiedCommonly used calibration methods include Platt Scaling (fitting a sigmoid function using logistic regression) and Isotonic Regression (fitting a non-parametric monotonic mapping using isotonic regression).
65%VerifiedProbability calibration refers to a post-processing technique that converts model output prediction scores into true probability estimates, with commonly used methods including Platt Scaling and Isotonic Regression.
65%UnverifiedCommon post-calibration techniques include Temperature Scaling, Platt Scaling, and Isotonic Regression, which remap raw outputs to more reliable probability estimates by fitting correction functions on an independent validation set without retraining the model.
70%UnverifiedPost-processing methods such as Temperature Scaling and Platt Scaling can re-adjust the output probability distribution without changing the model's prediction ranking.
50%UnverifiedConfidence calibration methods include post-processing techniques such as Platt Scaling and Temperature Scaling.
50%UnverifiedTechnical approaches to addressing poor model calibration include post-processing calibration methods such as temperature scaling and Platt Scaling, as well as uncertainty estimation techniques such as Bayesian neural networks and MC Dropout.
50%