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Numeric_​Scorer_​Solution

Numeric_Scorer

Numeric scorer: compare performance of two regression models

Exercise: Numeric Scorer1) Read the dataset ames_predicted_saleprice.csv. It contains information about houses in Ames (Iowa, USA).The "SalePrice" column contains the actual sale price. The “Prediction (Saleprice)” columns contain predictions for the price,performed by two different models - a Linear Regression (LR) and a Polynomial Regression (PL) .2) Evaluate the accuracy of the two models. Use two Numeric Scorer nodes.3) Group the error measures in a single table using the Column Appender node. - Which Error Measures are available? - Which model performed better and why? EvaluateLinear RegressionEvaluatePolynomial RegressionRead dataames_predicted_saleprice.csv Numeric Scorer Numeric Scorer Column Appender CSV Reader Exercise: Numeric Scorer1) Read the dataset ames_predicted_saleprice.csv. It contains information about houses in Ames (Iowa, USA).The "SalePrice" column contains the actual sale price. The “Prediction (Saleprice)” columns contain predictions for the price,performed by two different models - a Linear Regression (LR) and a Polynomial Regression (PL) .2) Evaluate the accuracy of the two models. Use two Numeric Scorer nodes.3) Group the error measures in a single table using the Column Appender node. - Which Error Measures are available? - Which model performed better and why? EvaluateLinear RegressionEvaluatePolynomial RegressionRead dataames_predicted_saleprice.csv Numeric Scorer Numeric Scorer Column Appender CSV Reader

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Created by: Emilio Silvestri
Created at: 2020-11-16
On NodePit since: 2025-01-05
Last update: 2025-03-29
Created with KNIME version: v4.7.0
Tags: GIDSexerciseacademiamodel evaluationTheGuideBook

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