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–in that it can reveal causes and effects of changes within a model, even if the model’s in...
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability methods because they do not know...
We draw motivation from these approaches and study imbalanced learning, explainability, and reliability of ML methods issues in the context of Materials Informatics applications. Our contributions In this paper, we take some first steps in addressing the challenge of building reliable and explainable ML...
Method 5: Local Surrogate (LIME) Global vs. Local Surrogate Methods 《Interpretability Methods in Machine Learning: A Brief Survey》翻译与解读 Interpretable ML models and “Black Boxes” Model-agnostic interpretability methods Several important model-agnostic interpretability ...
Machine Learning Explainability by Kaggle Learn Model Interpretability with DALEX Model Interpretation series by Dipanjan (DJ) Sarkar: The Importance of Human Interpretable Machine Learning Model Interpretation Strategies Hands-on Machine Learning Model Interpretation ...
the explainability of each module becomes crucial. For a large machine learning system, the explainability of the whole depends on the explainability of its parts. The transition from black-box machine learning to explainable machine learning needs a systematic evolution and upgrade, from theor...
explainability methods for machine learning systems for multimodal medical datasets: research proposal [Paper] analyzing the robustness of open-world machine learning [Paper] validation methods to promote real-world applicability of machine learning in medicine [Paper] machine learning approaches for...
machine learning (ML)healthcarediagnosisprognosisriskbrain diseasesARTIFICIAL-INTELLIGENCEALZHEIMERS-DISEASEBLACK-BOXIn recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area ...
using methods such as Shapley additive explanations (SHAP) to see which factors most influence the output. In this way, researchers can arrive at a clear picture of how the model makes decisions (explainability), even if they do not fully understand the mechanics of the complex neural network ...
Machine learning and cybersecurity use cases There are four ways ML is being used in cybersecurity: ML and facial recognition are used in authentication methods to protect an enterprise’s security. Antivirus programs may use AI and ML techniques to detect and block malware. ...