Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibration impact on the interpretability of the results. We present a use case of a patient in diabetes screening prediction scenario and visualize results using three different techniques to demonstrate the differences between calibrated and uncalibrated regularized regression model.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CHiQPM: Calibrated Hierarchical Interpretable Image Classification
Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human exper…
Image ClassificationLearning to Select Prototypical Parts for Interpretable Sequential Data Modeling
Prototype-based interpretability methods provide intuitive explanations of model prediction by comparing samples to a reference set of memorized exemplars or typical representatives in terms of similarity. In the field o…
DiversityAdaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images
Identifying cell types and subtypes in routine histopathology is fundamental for understanding disease. Existing tile-based models capture nuclear detail but miss the broader tissue context that influences cell identity.…
Model Explanations under Calibration
Explaining and interpreting the decisions of recommender systems are becoming extremely relevant both, for improving predictive performance, and providing valid explanations to users. While most of the recent interest ha…
modelRecommendation SystemsvalidAlike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but often lack feature-level granularity. We introduce a framework that in…
Feature Importance