Parameter Optimization for Classification

In this space we show both simple and complex workflows to learn how to fine-tune parameters for a generic classification model. Browse through those workflows to learn how to combine those nodes and components with any learner and predictor node which KNIME offers for ML classification.

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XAI Space

This space offers a collection of examples workflows for XAI. These workflows showcase how to use both: - Nodes from the KNIME Machine Learning Interpretability Extension: --> kni.me/e/IaoFfMScprMvBCY- - Model Interpretability Verified Components: --> hub.knime.com/knime/spaces/Examples/latest/~WMtQn1U91a-xzZY3/ These XAI examples can help you understand and interpret your machine learning model trained in KNIME. The workflows are divided into 2 main categories based on the ML Task : Classification and Regression. There are then also two sub-categories based on the type of training: AutoML and Custom Models. More infos on KNIME Blog: knime.com/blog/download-explainable-xai-solutions-hub

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Public

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