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JKISeason2-13_​rev00

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.

You ara a data scientist working for a real estate company, and hearda rumour that the "average number of rooms per dwelling" (RM) maybe connected to the "per capita crime rate" (CRIM) depending on thecity/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 toexperiment with a machine learning regression model and with a topicthat you have recently been studying: XAI. How are RM and CRIMconnected in Boston? Hint: Consider calculating the SHAP values ofeach independent feature using a SHAP loop. Hint 2: Consider usinga dependence plot to verify how RM and CRIM are connected visually. Node 1execute up-streambefore configuration Right Click > Interactive Viewtop: Dataset Samplemiddle: from AutoML componentbottom: Explainable Instances----output: ExplanationsPort 0 : train setPort 1: test setCreate BinsNormalizeSHAP : Look at the viewof this componentusing SHAP Summarizer Sampling weightSampled rowsShapley Values : Look at the viewof this componentusing k-means to summarize the data to n prototypes rowNode 1347CSV Reader AutoML XAI View Partitioning Auto-Binner Normalizer Dependence Plot Shapley ValuesLoop End SHAP Loop End Shapley ValuesLoop Start SHAP Loop Start Row Sampling Post-Processing Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesPredictor (Regression) Dependence Plot SHAP Summarizer Partitioning You ara a data scientist working for a real estate company, and hearda rumour that the "average number of rooms per dwelling" (RM) maybe connected to the "per capita crime rate" (CRIM) depending on thecity/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 toexperiment with a machine learning regression model and with a topicthat you have recently been studying: XAI. How are RM and CRIMconnected in Boston? Hint: Consider calculating the SHAP values ofeach independent feature using a SHAP loop. Hint 2: Consider usinga dependence plot to verify how RM and CRIM are connected visually. Node 1execute up-streambefore configuration Right Click > Interactive Viewtop: Dataset Samplemiddle: from AutoML componentbottom: Explainable Instances----output: ExplanationsPort 0 : train setPort 1: test setCreate BinsNormalizeSHAP : Look at the viewof this componentusing SHAP Summarizer Sampling weightSampled rowsShapley Values : Look at the viewof this componentusing k-means to summarize the data to n prototypes rowNode 1347CSV Reader AutoML XAI View Partitioning Auto-Binner Normalizer Dependence Plot Shapley ValuesLoop End SHAP Loop End Shapley ValuesLoop Start SHAP Loop Start Row Sampling Post-Processing Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesPredictor (Regression) Dependence Plot SHAP Summarizer Partitioning

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Created by: alxpei77
Created at: 2023-06-21
On NodePit since: 2023-06-27
Last update: 2024-04-27
Created with KNIME version: v4.7.4
Tags: JKISeason2-13

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