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extra 08 Regression Model

08 Regression Model - Solution
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? Top: train set (75%)Bottom: test set (25%)Random samplingTrain the modelto calculate monthly chargesApply the modelto the test setEvaluate modelperformanceWA Fn UseCTelco Customerchurn.csv Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Missing Value CSV Reader 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? Top: train set (75%)Bottom: test set (25%)Random samplingTrain the modelto calculate monthly chargesApply the modelto the test setEvaluate modelperformanceWA Fn UseCTelco Customerchurn.csvPartitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Missing Value CSV Reader

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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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