Icon

01_​Linear_​Regression_​solution

Linear Regression - Solution

Introduction to Machine Learning Algorithms course - Session 2
Solution to exercise 1
- Partition data into training and test set
- Train a linear regression model
- Apply the trained model to the test set
- Handle missing values
- Evaluate the model performance with the Numeric Scorer node

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...1) Add Partitioning node to File Reader output - Top port should have 70 % of the rows - Draw randomly such rows2) 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)?3) Add Regression Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports4) Remove rows with missing prediction5) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node housing dataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor File Reader Missing Value 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...1) Add Partitioning node to File Reader output - Top port should have 70 % of the rows - Draw randomly such rows2) 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)?3) Add Regression Predictor - Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports4) Remove rows with missing prediction5) Add Numeric Scorer to Regression Predictor Output - Reference Column: the column you learned - Predicted Column: the new column created by the predictor node housing dataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor File Reader 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: 2021-07-13
Last update: 2026-01-06
Created with KNIME version: v4.4.0
Tags: linear regressionmachine learningpredictioneducation

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

Try NodePit Runner!