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08 Regression Model

Exercise for training a model for numeric prediction.

Train and apply a linear regression model. Evaluate the performance with numeric scoring metrics.





Exercise: Linear Regression and Numeric Scoring Metrics1) Read the adult_joined.table file by executing the Table Reader and Missing Value nodes2) Partition the data into a training set (75 %) and test set (25 %). Draw randomly.3) Train a linear regression model on the training set to predict the weekly working hours. Use all other columns but the "ID"column for the prediction.4) Apply the model to the test set5) Evaluate the performance of the linear regression model with the Numeric Scorer node. Which proportion of the variance of the weekly working hours does the model explain? 20%How many hours is the mean absolute error of the model?8 hours partitionstrain modelNode 13EvaluateRead data adult_joined.table Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Table Reader Missing Value Exercise: Linear Regression and Numeric Scoring Metrics1) Read the adult_joined.table file by executing the Table Reader and Missing Value nodes2) Partition the data into a training set (75 %) and test set (25 %). Draw randomly.3) Train a linear regression model on the training set to predict the weekly working hours. Use all other columns but the "ID"column for the prediction.4) Apply the model to the test set5) Evaluate the performance of the linear regression model with the Numeric Scorer node. Which proportion of the variance of the weekly working hours does the model explain? 20%How many hours is the mean absolute error of the model?8 hours partitionstrain modelNode 13EvaluateRead data adult_joined.tablePartitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Table Reader Missing Value

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2023-08-28
Last update: 2026-01-06
Created with KNIME version: v4.3.2
Tags: r-squarederror metriclinear regression

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