The Penalized Inverse Probability Measure for Conformal Classification

The deployment of safe and trustworthy machine learning systems, and particularly complex black box neural networks, in real-world applications requires reliable and certified guarantees on their performance. The conformal prediction framework offers such formal guarantees by transforming any point into a set predictor with valid, finite-set, guarantees on the coverage of the true at a chosen level of confidence. Central to this methodology is the notion of the nonconformity score function that assigns to each example a measure of ''strangeness'' in comparison with the previously seen observations. While the coverage guarantees are maintained regardless of the nonconformity measure, the point predictor and the dataset, previous research has shown that the performance of a conformal model, as measured by its efficiency (the average size of the predicted sets) and its informativeness (the proportion of prediction sets that are singletons), is influenced by the choice of the nonconformity score function. The current work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness. Through toy examples and empirical results on the task of crop and weed image classification in agricultural robotics, the current work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency.

Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification

As deep learning predictive models become an integral part of a large spectrum of precision agricultural systems, a barrier to the adoption of such automated solutions is the lack of user trust in these highly complex, opaque and uncertain models. Indeed, deep neural networks are not equipped with any explicit guarantees that can be used to certify the system's performance, especially in highly varying uncontrolled environments such as the ones typically faced in computer vision for agriculture.Fortunately, certain methods developed in other communities can prove to be important for agricultural applications. This article presents the conformal prediction framework that provides valid statistical guarantees on the predictive performance of any black box prediction machine, with almost no assumptions, applied to the problem of deep visual classification of weeds and crops in real-world conditions. The framework is exposed with a focus on its practical aspects and special attention accorded to the Adaptive Prediction Sets (APS) approach that delivers marginal guarantees on the model's coverage. Marginal results are then shown to be insufficient to guarantee performance on all groups of individuals in the population as characterized by their environmental and pedo-climatic auxiliary data gathered during image acquisition.To tackle this shortcoming, group-conditional conformal approaches are presented: the ''classical'' method that consists of iteratively applying the APS procedure on all groups, and a proposed elegant reformulation and implementation of the procedure using quantile regression on group membership indicators. Empirical results showing the validity of the proposed approach are presented and compared to the marginal APS then discussed.