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JKISeason2-13

JKISeason2-13
https://www.knime.com/just-knime-it

Level : Medium

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.

Author: Keerthan Shetty

Dataset: Boston Real Estate Data in the KNIME Hub

using k-means to summarize the data to n prototypes rowNode 280/20Node 6100 rowsSHAP : Look at the viewof this componentNode 87right > SHAPNode 89Node 93Node 98Node 99SHAP Summarizer CSV Reader Partitioning SHAP Loop Start Row Sampling Dependence Plot Joiner Column Rename(Regex) Numeric Scorer SHAP Loop End Linear RegressionLearner RegressionPredictor using k-means to summarize the data to n prototypes rowNode 280/20Node 6100 rowsSHAP : Look at the viewof this componentNode 87right > SHAPNode 89Node 93Node 98Node 99SHAP Summarizer CSV Reader Partitioning SHAP Loop Start Row Sampling Dependence Plot Joiner Column Rename(Regex) Numeric Scorer SHAP Loop End Linear RegressionLearner RegressionPredictor

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: seavi
Created at: 2023-06-24
On NodePit since: 2023-06-27
Last update: 2024-12-23
Created with KNIME version: v4.6.0
Tags: JKISeason2-13Just KNIME It

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