However, most of these systems lack transparency and explainability, making it difficult to understand their internal processes and consequently to trust their decisions and predictions. To address this problem, we propose an easy-to-use machine learning model based on an intuitive geometric approach ...
Train a model Explore AI model capabilities Use Generative AI Responsibly develop & monitor Responsible AI overview What is Responsible AI? Model interpretability Fairness in Machine Learning Causal analysis Assess errors in ML models Understand your datasets ...
As machine learning expands into sensitive domains like healthcare, finance, and autonomous vehicles, interpretability and explainability will only grow in importance. SHAP values offer a flexible, consistent approach to explaining predictions and model behavior. It can be used to gain insights into how...
However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This ...
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...
MLPER-13: Evaluate model explainabilityPDFRSS Evaluate model performance as constrained by the explainability requirements of the business. Compliance requirements, business objectives, or both might require that the inferences from a model be directly explainable. Evaluate the explainability needs, and ...
和Miller提出的相似,我认为区分interpretability/explainability和explanation是有意义的。我会使用”explanation”来解释单个预测。如果想了解人类认为的好的解释是什么,请参阅有关解释(explanation)的部分。 2.1 可解释的重要性 如果机器学习模型表现良好,为什么我们不相信模型并忽略它做出某个决定的原因? “问题在于单个...
参考: https://www.kaggle.com/learn/machine-learning-explainability 这个课程将讲解如何从复杂的机器学习模型中解释这些发现。 这些发现有许多用途,包括 Permutation Importance置换重要性 统计每个feature的重要程度
Interpreting Machine Learning Models with the iml Package 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 ...
設定Azure Machine Learning 自動化 ML,以使用 CLI 第 2 版和 Python SDK 第 2 版定型電腦視覺模型。