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08 Regression Model

08 Regression Model
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 varianceof the weekly working hours does the model explain? How many hours is the mean absolute error of the model? The R^2value which is about 20%. The mean absolute error is about 8 hours Read data adult_joined.tableNode 13Node 14Node 15Node 16 Missing Value Table Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer 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 varianceof the weekly working hours does the model explain? How many hours is the mean absolute error of the model? The R^2value which is about 20%. The mean absolute error is about 8 hours Read data adult_joined.tableNode 13Node 14Node 15Node 16Missing Value Table Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2022-02-27
Last update: 2024-11-21
Created with KNIME version: v4.5.1
Tags: r-squarederror metriclinear regression

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