Icon

Regression SHAP Shapley

Linear Regression
Linear Regresion: price prediction Exercise: Linear RegressionIn this exercise we will predict the price of an house in Ames (Iowa, USA) given a number of features (size, neighborhood, heating...) using Linear Regression.1) Read dataset AmesHousing_simple.csv. It contains information about houses sold in Ames (only numerical values) as well as the SalePrice.2) Add Partitioning node to File Reader output - Top port should have 70 % of the rows - Draw randomly such rows3) Add Linear Regression Learner to top output port of Partitioning node - Select price column to be learned - Execute the node and open its scatter plot view. Which column is most correlated to the price (column selection tab)?4) Add Regression Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports5) Remove rows with missing prediction6) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node Node 64Node 65Node 66Node 67Node 68Node 70 Partitioning Linear RegressionLearner RegressionPredictor CSV Reader SHAP Loop Start Row Sampling SHAP Loop End Shapley ValuesLoop Start RegressionPredictor Shapley ValuesLoop End Linear Regresion: price prediction Exercise: Linear RegressionIn this exercise we will predict the price of an house in Ames (Iowa, USA) given a number of features (size, neighborhood, heating...) using Linear Regression.1) Read dataset AmesHousing_simple.csv. It contains information about houses sold in Ames (only numerical values) as well as the SalePrice.2) Add Partitioning node to File Reader output - Top port should have 70 % of the rows - Draw randomly such rows3) Add Linear Regression Learner to top output port of Partitioning node - Select price column to be learned - Execute the node and open its scatter plot view. Which column is most correlated to the price (column selection tab)?4) Add Regression Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports5) Remove rows with missing prediction6) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node Node 64Node 65Node 66Node 67Node 68Node 70 Partitioning Linear RegressionLearner RegressionPredictor CSV Reader SHAP Loop Start Row Sampling SHAP Loop End Shapley ValuesLoop Start RegressionPredictor Shapley ValuesLoop End

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2022-07-08
Last update: 2024-05-01
Created with KNIME version: v4.5.2
Tags: GIDSacademiaexerciseregressionTheGuideBook

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!