Interpreting and explaining machine learning models
Interpreting and explaining machine learning models is essential for understanding their predictions and making them more transparent and trustworthy, especially in applications where interpretability is critical. This is an ongoing process that requires collaboration between data scientists, domain experts, and stakeholders. The choice of interpretation techniques depends on the model type, problem domain, and level of transparency required for the application. It’s important to strike a balance between model complexity and interpretability, depending on the specific use case.
Understanding saliency maps
Saliency maps are a visualization technique that’s used in computer vision and deep learning to understand and interpret neural network predictions, particularly in image classification and object recognition tasks. Saliency maps help identify which regions of an input image or feature map are most relevant to...
