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JKISeason2-13_​sryu

Challenge 13:

Description: You ara a data scientist working for a real estate company, and heard a rumour that the "average number of rooms per dwelling" (RM) may be connected to the "per capita crime rate" (CRIM) depending on the city/town. You then decide to investigate if this is the case for Boston, the city where you live and work from. To this end, you decide to experiment with a machine learning regression model and with a topic that you have recently been studying: XAI. How are RM and CRIM connected in Boston? Hint: Consider calculating the SHAP values of each independent feature using a SHAP loop. Hint 2: Consider using a dependence plot to verify how RM and CRIM are connected visually.

dataPort 0 : train setPort 1: test setusing SHAP Summarizer Sampling weightPrefix "SHAP"SHAP valuesfeature wiseusing k-means to summarize the data to n prototypes rowSHAP : Look at the viewof this componentNode 1352Node 1353CSV Reader Partitioning SHAP Loop Start Column Rename(Regex) Column Filter Gradient Boosted TreesLearner (Regression) SHAP Summarizer Gradient Boosted TreesPredictor (Regression) SHAP Loop End Dependence Plot Joiner RowID dataPort 0 : train setPort 1: test setusing SHAP Summarizer Sampling weightPrefix "SHAP"SHAP valuesfeature wiseusing k-means to summarize the data to n prototypes rowSHAP : Look at the viewof this componentNode 1352Node 1353CSV Reader Partitioning SHAP Loop Start Column Rename(Regex) Column Filter Gradient Boosted TreesLearner (Regression) SHAP Summarizer Gradient Boosted TreesPredictor (Regression) SHAP Loop End Dependence Plot Joiner RowID

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Created by: sryus
Created at: 2023-06-23
On NodePit since: 2023-06-27
Last update: 2024-12-21
Created with KNIME version: v4.7.4
Tags: JKISeason2-13

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