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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


Exercise: Linear RegressionIn this exercise we will predict the price of a house in Ames (Iowa, USA) given a number of features: size, neighborhood, heating...1) Add Partitioning node to CSV Reader output port- Top port should have 70 % of the rows- Draw randomly such rows2) Add Linear Regression Learner node 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 node- Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports4) Remove rows with missing prediction (Missing Value node)5) Add Numeric Scorer node to the Regression Predictor output port- Reference Column: the column you learned- Predicted Column: the new column created by the predictor node Price Prediction: Linear Regression housing dataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor CSV Reader Missing Value Exercise: Linear RegressionIn this exercise we will predict the price of a house in Ames (Iowa, USA) given a number of features: size, neighborhood, heating...1) Add Partitioning node to CSV Reader output port- Top port should have 70 % of the rows- Draw randomly such rows2) Add Linear Regression Learner node 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 node- Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports4) Remove rows with missing prediction (Missing Value node)5) Add Numeric Scorer node to the Regression Predictor output port- Reference Column: the column you learned- Predicted Column: the new column created by the predictor node Price Prediction: Linear Regression housing dataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor CSV Reader Missing Value

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2023-04-06
Last update: 2024-05-01
Created with KNIME version: v4.6.1
Tags: linear regressionmachine learningpredictioneducation

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