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JKISeason3-18_​Explaining Cancer Predictions

Explaining Cancer Predictions

Challenge 18

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

URL: Dataset https://hub.knime.com/alinebessa/spaces/Just%20KNIME%20It!%20Season%203%20-%20Datasets/Challenge%2018%20-%20Dataset~JMWDCxY4oCz_eK_o/
URL: Compute Local Model-agnostic Explanations (LIMEs) https://hub.knime.com/knime/spaces/Examples/04_Analytics/17_Machine_Learning_Interpretability/01_Compute_LIMEs~3NciU4lnW6e4RMk1/current-state
URL: LIME Loop Nodes with AutoML https://hub.knime.com/knime/spaces/XAI%20Space/Classification/AutoML/07_Compute_LIMEs~smwAHMlwad23OHK4/current-state

Node 1Node 3Node 11Node 12Node 15Node 20training a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 205Node 206Node 267Visualizing 1 LIMENode 289Node 290Node 291Node 292Node 293Node 294Node 295training a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 297Node 298CSV Reader Random Forest Learner(Regression) LIME Loop Start Partitioning Row Sampling Random Forest Predictor(Regression) Compute LIME Column Filter Column Filter Table Transposer Column Filter RowID Loop End Row Filter(deprecated) Post-Processing Bar Chart(JavaScript) VisualizeExplanations ExplanationsPost-processing CSV Reader Random Forest Learner(Regression) Partitioning Row Sampling Column Filter Column Filter Random Forest Predictor(Regression) Compute LIME LIME Loop Start Loop End Node 1Node 3Node 11Node 12Node 15Node 20training a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 205Node 206Node 267Visualizing 1 LIMENode 289Node 290Node 291Node 292Node 293Node 294Node 295training a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 297Node 298CSV Reader Random Forest Learner(Regression) LIME Loop Start Partitioning Row Sampling Random Forest Predictor(Regression) Compute LIME Column Filter Column Filter Table Transposer Column Filter RowID Loop End Row Filter(deprecated) Post-Processing Bar Chart(JavaScript) VisualizeExplanations ExplanationsPost-processing CSV Reader Random Forest Learner(Regression) Partitioning Row Sampling Column Filter Column Filter Random Forest Predictor(Regression) Compute LIME LIME Loop Start Loop End

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