可解释性(Interpretable):它要求我们所使用的可解释模型是人类易于理解的,比如一颗小的决策树是很好理解的,一个不懂机器学习的人也知道这颗树在做什么,但这个模型它的准确率是不够高的,那我们要提升准确率,通常我们会采用一片决策树,这个时候,模型就变得不可解释了。我们可以看到作者这里对可解释的想法跟我前面所...
Furthermore, explainability is a rapidly evolving field, and there is still ongoing research to develop robust and scalable explainability techniques. As machine learning models continue to advance, new challenges are likely to arise in terms of providing interpretable explanations that are meaningful to...
whereasexplainabilityinvolves describing the model's decision-making in an understandable way. Interpretable ML techniques are typically used by data scientists and other ML practitioners, where explainability is more often intended to help non-experts understand machine learning models. A so-calledbla...
And (2) what does it mean for a network to be interpretable ? We argue that accounts of "explanation" tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they ...
3. Data Understanding:Annotation adds context and understanding to raw data. By assigning labels or annotations to data, it becomes more interpretable and insightful. For example, in image recognition, annotations provide information about the objects present in an image, making it easier for the mo...
Interpretable Machine Learning Serg Masis talks about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems and the different types of technical and non-technical solutions to tackling bias. Adel Nehme 51 minSee...
: An Interpretable Machine Learning Approach : An inter- pretable machine learning approach. In PloS one.L. Arras, F. Horn, G. Montavon, K.-R. Mu¨ller, and W. Samek, ""what is relevant in a text document?": An interpretable machine learning approach," Plos ONE,... L Arras,F ...
Advanced methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) provide model-agnostic interpretations, while feature selection techniques (filter, wrapper, and embedded methods) also play a role. Darryl Posted 2 months ago arrow_drop_up0 more_...
A random forest is a collection of decision trees that gather results from multiple predictors. It's better at generalization, but less interpretable when compared with decision trees. Asupport vector machinefinds a line that separates data in a particular set into specific classes during model trai...
With explainable AI – as well as interpretable machine learning – organizations can gain access to AI technology’s underlying decision-making and are empowered to make adjustments. Explainable AI can improve the user experience of a product or service by helping the end user trust that the AI...