Icon

Regression_​Tree_​Solution

Regression Tree - Solution
Regresion Tree: price prediction Exercise: Regression TreeIn this exercise we will predict the price of an house in Ames (Iowa, USA) given a number of features (size, neighborhood, heating...) using a Regression Tree.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 Simple Regression Tree Learner to top output port of Partitioning node - Select price column to be learned - Execute the node and open its decision tree view. Which column is used in the beginning of the tree?4) Add Simple Regression Tree Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports5) Remove rows with missing values6) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node Read AmesHousing_simple.csv Partitioning Numeric Scorer File Reader Simple RegressionTree Learner Simple RegressionTree Predictor Missing Value Regresion Tree: price prediction Exercise: Regression TreeIn this exercise we will predict the price of an house in Ames (Iowa, USA) given a number of features (size, neighborhood, heating...) using a Regression Tree.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 Simple Regression Tree Learner to top output port of Partitioning node - Select price column to be learned - Execute the node and open its decision tree view. Which column is used in the beginning of the tree?4) Add Simple Regression Tree Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports5) Remove rows with missing values6) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node Read AmesHousing_simple.csvPartitioning Numeric Scorer File Reader Simple RegressionTree Learner Simple RegressionTree Predictor Missing Value

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: 2022-10-28
Last update: 2024-11-23
Created with KNIME version: v4.3.0
Tags: regressionmachine learningeducationGIDSacademiaexerciseTheGuideBook

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

Try NodePit Runner!