Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysismachine learning explainabilitysport medicinehamstring injuriessoccer playerXGBoost
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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...
The machine learning explainability in materials science section reviews XAI techniques with recent materials science application examples. Many of the model explanation techniques we discuss work with image data and convolution neural networks (CNNs), but other data types (e.g., tabular data, ...
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. ...
It is possible, however, to determinehowa machine learning algorithm arrived at its conclusions. This ability, otherwise known as “interpretability,” is a very active area of investigation among AI researchers in both academia and industry. It differs slightly from “explainability”–answeringwhy–...
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...
the reasoning behind a seemingly correct decision might be totally wrong. For the fields such as medical treatment, nuclear, and aerospace, understanding the supporting facts of decisions is a prerequisite for applying machine learning techniques, as explainability implies trustworthiness and reliab...
ML interpretability and explainability While RF, XGBoost and DNN have demonstrated high-prediction accuracy than simpler models such as DT in prior studies, they are also regarded as ‘black-box’ models since it is difficult for humans to comprehend their behavior in predicting the outcome. Interpr...
Training a machine learning model is an iterative process, and does not always guarantee a robust model. Using an inference model can provide better performance compared to an in-house model. Nowadays, model explainability and bias mitigation are crucial, and inference models may need to be ...