Future posts will cover more techniques in detail! If you trained a model in Python and you want to explain it in KNIME, we recommend “Codeless Counterfactual Explanations for Codeless Deep Learning” on KNIME Blog. If you're new to XAI, consider the LinkedIn Learning course “Machine lear...
This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) networks were trained to predict the target selection ...
Kernel regression is a supervised learning problem where one estimates a function from a number of observations. For our setup, let \({\mathcal{D}}={\{{{\bf{x}}}^{\mu },{y}^{\mu }\}}_{\mu = 1}^{P}\) be a sample of P observations drawn from a probability distribution on...
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation. PLOS Computational Biology. 2014; 10(11):1–29. 9. Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ. Performance-optimized hierar- chical models predict neural responses...
2.1 Text-to-Text Framework In addition to producing state-of-the-art results on explainability datasets, this approach also allows for both "semi-supervised" training (where explanations are only provided on a subset of the dataset) and for A text-to-text model follows the sequence-to-...
In the new space, we showcase how to use the various KNIME components and nodes designed for model interpretability. You can find examples in thisXAI Space, divided into two primary groups based on the ML task: Classification - supervised ML algorithms with a categorical (string) target value...