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JKISeason2-13

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.

Load data30/70execute up-streambefore configurationNode 874using k-means to summarize the data to n prototypes rowusing SHAP Summarizer Sampling weightNode 877Node 878ResetRowIDfilter SHAP values& perfix "SHAP"Only filterCRIM & RM valuesPrepareplot dataCSV Reader Partitioning Dependence Plot AutoML (Regression) Row Sampling SHAP Summarizer SHAP Loop Start Workflow Executor SHAP Loop End RowID Python Script Column Filter Joiner Load data30/70execute up-streambefore configurationNode 874using k-means to summarize the data to n prototypes rowusing SHAP Summarizer Sampling weightNode 877Node 878ResetRowIDfilter SHAP values& perfix "SHAP"Only filterCRIM & RM valuesPrepareplot dataCSV Reader Partitioning Dependence Plot AutoML (Regression) Row Sampling SHAP Summarizer SHAP Loop Start Workflow Executor SHAP Loop End RowID Python Script Column Filter Joiner

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Created by: elimi
Created at: 2023-06-21
On NodePit since: 2023-06-30
Last update: 2024-04-26
Created with KNIME version: v4.7.4
Tags: JKISeason2-13

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