51 research outputs found
Non-Parametric Calibration of Probabilistic Regression
The task of calibration is to retrospectively adjust the outputs from a
machine learning model to provide better probability estimates on the target
variable. While calibration has been investigated thoroughly in classification,
it has not yet been well-established for regression tasks. This paper considers
the problem of calibrating a probabilistic regression model to improve the
estimated probability densities over the real-valued targets. We propose to
calibrate a regression model through the cumulative probability density, which
can be derived from calibrating a multi-class classifier. We provide three
non-parametric approaches to solve the problem, two of which provide empirical
estimates and the third providing smooth density estimates. The proposed
approaches are experimentally evaluated to show their ability to improve the
performance of regression models on the predictive likelihood
Precision-Recall-Gain Curves:PR Analysis Done Right
Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier’s performance. Perhaps inspired by the many advantages of receiver op-erating characteristic (ROC) curves and the area under such curves for accuracy-based performance assessment, many researchers have taken to report Precision-Recall (PR) curves and associated areas as performance metric. We demonstrate in this paper that this practice is fraught with difficulties, mainly because of in-coherent scale assumptions – e.g., the area under a PR curve takes the arithmetic mean of precision values whereas the Fβ score applies the harmonic mean. We show how to fix this by plotting PR curves in a different coordinate system, and demonstrate that the new Precision-Recall-Gain curves inherit all key advantages of ROC curves. In particular, the area under Precision-Recall-Gain curves con-veys an expected F1 score on a harmonic scale, and the convex hull of a Precision-Recall-Gain curve allows us to calibrate the classifier’s scores so as to determine, for each operating point on the convex hull, the interval of β values for which the point optimises Fβ. We demonstrate experimentally that the area under traditional PR curves can easily favour models with lower expected F1 score than others, and so the use of Precision-Recall-Gain curves will result in better model selection.
Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View
In driving scenarios with poor visibility or occlusions, it is important that
the autonomous vehicle would take into account all the uncertainties when
making driving decisions, including choice of a safe speed. The grid-based
perception outputs, such as occupancy grids, and object-based outputs, such as
lists of detected objects, must then be accompanied by well-calibrated
uncertainty estimates. We highlight limitations in the state-of-the-art and
propose a more complete set of uncertainties to be reported, particularly
including undetected-object-ahead probabilities. We suggest a novel way to get
these probabilistic outputs from bird's-eye-view probabilistic semantic
segmentation, in the example of the FIERY model. We demonstrate that the
obtained probabilities are not calibrated out-of-the-box and propose methods to
achieve well-calibrated uncertainties
Precision-Recall-Gain Curves: PR Analysis Done Right
Abstract Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier's performance. Perhaps inspired by the many advantages of receiver operating characteristic (ROC) curves and the area under such curves for accuracybased performance assessment, many researchers have taken to report PrecisionRecall (PR) curves and associated areas as performance metric. We demonstrate in this paper that this practice is fraught with difficulties, mainly because of incoherent scale assumptions -e.g., the area under a PR curve takes the arithmetic mean of precision values whereas the F β score applies the harmonic mean. We show how to fix this by plotting PR curves in a different coordinate system, and demonstrate that the new Precision-Recall-Gain curves inherit all key advantages of ROC curves. In particular, the area under Precision-Recall-Gain curves conveys an expected F 1 score on a harmonic scale, and the convex hull of a PrecisionRecall-Gain curve allows us to calibrate the classifier's scores so as to determine, for each operating point on the convex hull, the interval of β values for which the point optimises F β . We demonstrate experimentally that the area under traditional PR curves can easily favour models with lower expected F 1 score than others, and so the use of Precision-Recall-Gain curves will result in better model selection
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