Level: Hard
Description: You work as a researcher creating models to identify whether a breast tumor is benign or malign, based on anonymized patient data. Besides obtaining a classifier that works very well for both benign and malign cases, you must be able to explain how different feature values impact your results. Experiment with LIME and visualization techniques to explain your predictions and make your research more transparent. Hint: Learn more about this problem's data attributes here.
Author: Keerthan Shetty
Dataset: Breast Tumor Data in the KNIME Community Hub
URL: Datasets https://hub.knime.com/s/JMWDCxY4oCz_eK_o
URL: JKISeason3-18 https://www.knime.com/just-knime-it?pk_vid=f1a9625dd14a14c5171698895027e10b
URL: This challenge thread https://forum.knime.com/t/solutions-to-just-knime-it-challenge-18-season-3/83116?pk_vid=4e602e8568914d2d1726067200168798
URL: Explain Stroke Prediction Models with LIME in KNIME https://www.knime.com/blog/XAI-LIME-stroke-prediction
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