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01_​Linear_​Regression_​solution

Linear Regression - solution
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 Node 64Node 66Node 67Node 68Node 69Node 70Node 71Node 72Node 74Node 75 Variance InflationFactor (VIF) filter Forward FeatureSelection Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor Missing Value CSV Reader Statistics Missing Value Normalizer Partitioning Linear RegressionLearner ReferenceColumn Filter RegressionPredictor VIFs Numeric Scorer CSV Reader 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 Node 64Node 66Node 67Node 68Node 69Node 70Node 71Node 72Node 74Node 75 Variance InflationFactor (VIF) filter Forward FeatureSelection Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor Missing Value CSV Reader Statistics Missing Value Normalizer Partitioning Linear RegressionLearner ReferenceColumn Filter RegressionPredictor VIFs Numeric Scorer CSV Reader

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2022-11-15
Last update: 2024-04-18
Created with KNIME version: v4.6.3
Tags: linear regressionmachine learningpredictioneducation

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