Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patientCardiac arrestMachine learningQuantitative EEGHeart rate variabilityMulti-modal evaluationSHAP analysisEarly and accurate prediction of neurological outcomes in comatose ...
we assemble and analyse a continent-wide database of field experiments spanning 10 years and hundreds of thousands of machine-phenotyped populations of ten major crop species. Training an ensemble of machine learning models, using thousands of variables capturing weather, ground sensor, soil, chemi...
Goal of Explainable ML 对于解释性方面有些人的想法是:一定要完全知道ML model如何工作的,但是对于我们人脑来说,我们并不完全知道人脑是如何工作的,但是我们往往会相信人脑做出的决策。所以对于可解释的定义,我们认为模型的解释性被人所接受(你的客户或者老板等),就可以说明模型解释性很强。 Explainable ML 解释性有...
4 Using a model to explain another 现在使用一个interpretable model来模仿另外一个uninterpretable model; 下图中的 Black Box为 uninterpretable model, 比如Neural Network,蓝色方框是一个interpretable model,比如Linear model; 现在的目标是使用相同的输入 实际上并不能使用linear model来模拟整个neural network,但可...
Model Explaination 1. 为什么要解释算法 在某些场景,我们在使用一些机器学习模型处理数据以得到结果时,往往也会寻求一个解释,也就是数据的结果是从何而来的。 举个例子,医生在使用医疗诊断的模型获得诊断结果时。医生或是病人不可能仅仅依靠得到的结果就决定病情。重要的是,通过结果得到的过程,也就是结果的解释最终确...
Explainable Machine Learning P1-Why Does the Model Make This Prediction 到目前為止,我们已经训练了很多很多的模型,你可能我们训练过影像辨识的模型,给它一张图片,它会给你答案,但我们并不满足於此,接下来我们要机器给我们,它得到答案的理由,这个就是 Explainable 的 Machine Learning Why we need Explainable ML...
3、GLOBAL EXPLANATION:EXPLAIN THE WHOLE MODEL Question: What do you think a “cat” looks like? 4、Using a model to explain another 1、Explainable ML 1)是什么? EXPLAINABLEMACHINE LEARNING :让机器在判断input的同时,需要知道为什么这样判断。
Model Training Efficiency:By reducing the number of features, dimensionality reduction can significantly speed up the training of machine learning models, making them computationally more efficient. Overfitting Prevention:It can help mitigate the risk of overfitting by reducing noise and removing less relev...
Objective This study aimed to enhance the performance of risk assessment models in this patient population by developing a predictive model for adverse outcomes using various machine learning (ML) techniques. Methods This multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF ...
* Model-agnostic explanation * Case-based explanation * Evaluation metrics * Empirical research on explainability * Regulations and legal aspects of XAI * Use of knowledge graphs in XAI research * Explainability of relational learning * Applications of all of the above (in text classification, disinf...