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JKISeason2-13

Challenge 13: Housing

Challenge 13: HousingDescription: 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 onnected tothe "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 thisend, 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 inBoston? 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 andCRIM are connected visually. housing.csv SHAPusing SHAP Summarizer Sampling weightSampled 20 rows20 prototypes70-30Shapley ValuesCSV Reader Dependence Plot Shapley ValuesLoop End SHAP Loop End Shapley ValuesLoop Start SHAP Loop Start Row Sampling Post-Processing SHAP Summarizer Partitioning Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesPredictor (Regression) Dependence Plot Challenge 13: HousingDescription: 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 onnected tothe "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 thisend, 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 inBoston? 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 andCRIM are connected visually. housing.csv SHAPusing SHAP Summarizer Sampling weightSampled 20 rows20 prototypes70-30Shapley ValuesCSV Reader Dependence Plot Shapley ValuesLoop End SHAP Loop End Shapley ValuesLoop Start SHAP Loop Start Row Sampling Post-Processing SHAP Summarizer Partitioning Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesPredictor (Regression) Dependence Plot

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Created by: yilmaer
Created at: 2023-06-22
On NodePit since: 2023-06-27
Last update: 2024-04-28
Created with KNIME version: v4.7.2
Tags: JKISeason2-13

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