Conclusion: In comparison to previous work, improved performances for ADNI and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI) classification using brain volumes. The Shapley values and the feature importances showed moderate to strong correlations....
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity Interpretable Predictive Value of Including HDL-2b and HDL-3 in an Explainable Boosting Machine Model for Multiclass Classification of Coronary Artery Stenosis Severity in Acute Myocardial Infarction Patients ...
What is classification in machine learning? Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning). Perform a hierarchical clustering of the following one-dimensional...
KNIME can provide you with no-code XAI techniques to explain your machine learning model. We have released an XAI space on the KNIME Hub dedicated to example workflows with all the available XAI techniques for both ML regression and classification tasks. The public space with XAI example workflow...
Iris classification- A basic demonstration using the popular iris species dataset. It explains predictions from six different models in scikit-learn usingshap. Documentation notebooks These notebooks comprehensively demonstrate how to use specific functions and objects. ...
This MATLAB function returns the gradient-weighted class activation mapping (Grad-CAM) map of the change in the classification score of input X, when the network net evaluates the class score for the class given by classIdx.
https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification Follow 0.0 (0) 319 Downloads Updated25 Dec 2020 View License on GitHub Share Open in MATLAB Online Download Class Activation Mapping(CAM) is a good method to explain why the model classify th...
We present some applications to classification and prediction with convex function classes, and with kernel classes in particular. CAS-1 JCR-Q1 SCIE 304 被引用 · 0 笔记 引用 Model Selection and Error Estimation Peter L. BartlettStéphane BoucheronGábor Lugosi Machine Learning Jan 2002 We study ...
Different techniques of computer vision Image classification Object detection Object tracking Semantic segmentation Instance segmentation Use cases Defect detection Metrology Intruder detection Assembly verification Screen reader
Related machine learning frameworks In terms of purpose, ExplAIn is related to existing algorithms for visualizing/interpreting what image classification CNNs have learnt. Given a trained classification CNN and an input image, these algorithms compute the influence of each pixel on CNN predictions. In...