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08 Regression Model

08 Regression Model
Exercise: Linear Regression and Numeric Scoring Metrics1) Read the telco-customer-churn.csv file by executing the CSV Reader node2) Partition the data into a training set (70%) and test set (30%). daw randomly.3) Train a linear regression model on the training set to predict the churn. Use all other columns but the "customerID"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 churn does the model explain? 27.9% of the variance of the churn is explained by the model.What is the mean absolute error of the model? 0.299 telco-customer-churn.csvPartition the data into a training set (70 %) and test set (30 %). Drawn randomly.Train the model to predict the churnApply the model to the test setEvaluate the performance of the linear regression modelconvert churn to binary CSV Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Missing Value Rule Engine Exercise: Linear Regression and Numeric Scoring Metrics1) Read the telco-customer-churn.csv file by executing the CSV Reader node2) Partition the data into a training set (70%) and test set (30%). daw randomly.3) Train a linear regression model on the training set to predict the churn. Use all other columns but the "customerID"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 churn does the model explain? 27.9% of the variance of the churn is explained by the model.What is the mean absolute error of the model? 0.299 telco-customer-churn.csvPartition the data into a training set (70 %) and test set (30 %). Drawn randomly.Train the model to predict the churnApply the model to the test setEvaluate the performance of the linear regression modelconvert churn to binaryCSV Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Missing Value Rule Engine

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2022-02-26
Last update: 2024-12-17
Created with KNIME version: v4.5.1
Tags: r-squarederror metriclinear regression

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