Post-hoc explainability techniques in machine learning are designed to provide explanations for complex, black-box models that lack intrinsic interpretability. These techniques aim to approximate the decision-making process of the model and shed light on the factors that contributed to its predictions. ...
one is that the logit function has the nice connection to odds. a second is that the gradients of the logit and sigmoid are simple to calculate. The reason why this is important is that many optimization and machine learning techniques make use of gradients, for example when estimating paramet...
Kakadiaris, I. A.et al.Machine learning outperforms ACC/AHA CVD risk calculator in MESA.J. Am. Heart Assoc.7, e009476 (2018). ArticleGoogle Scholar Kim, J. O.et al.Machine learning-based cardiovascular disease prediction model: A cohort study on the Korean national health insurance service...
A total of 133 relevant studies were identified and analyzed out of a total of 682 found in the initial search, in which the explainability of ML models in the medical context was studied, identifying 11 ML models and 12 explainability techniques applied in the study of 20 brain diseases....
Learning in big data: Introduction to machine learning. In Knowledge Discovery in Big Data from Astronomy and Earth Observation; Elsevier: Amsterdam, The Netherlands, 2020; pp. 225–249. [Google Scholar] Burkart, N.; Huber, M.F. A survey on the explainability of supervised machine learning. ...
by human beings has become a priority due to rapid advances in and adoption of sophisticated ML techniques, such as generative AI. Researchers at AI labs such as Anthropic have madeprogressin understanding how generative AI models work, drawing on interpretability and explainability techniques. ...
Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption...
Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient's risk of ...
Some disadvantages include the potential for biased data, overfitting data, and lack of explainability. Machine Learning Algorithms There are a variety of machine learning algorithms available and it is very difficult and time consuming to select the most appropriate one for the problem at hand. Thes...
Furthermore, we found that the model's own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-...