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Exercise 2 Training Algorithms for Numeric Prediction

This workflow shows a hands-on exercise in the L4-ML Introduction to Machine Learning Algorithms self-paced course

Task 1: Inspect bivariate relationships visually1. Draw one predictor feature vs target in a scatter plot2. Change the axis column via the view's menu Task 2: Compare the performances of a linear regression model and a regression treeon linear data1. Partition the data into training (70%) and test (30%) sets. Draw randomly.2. Predict the target column using the following algorithms:- Linear Regression- Simple Regression Tree3. Assess the performances of the models Task 3: Compare the performances of a linear regression model and a regression tree on non-linear data 1. Access the output of the "Make data non-linear" metanode and visualize the feature column vs the target column in ascatter plot2. Repeat steps 1-3 from Task 2 on this data readnumeric-prediction-data.table Make datanon-linear Table Reader Task 1: Inspect bivariate relationships visually1. Draw one predictor feature vs target in a scatter plot2. Change the axis column via the view's menu Task 2: Compare the performances of a linear regression model and a regression treeon linear data1. Partition the data into training (70%) and test (30%) sets. Draw randomly.2. Predict the target column using the following algorithms:- Linear Regression- Simple Regression Tree3. Assess the performances of the models Task 3: Compare the performances of a linear regression model and a regression tree on non-linear data 1. Access the output of the "Make data non-linear" metanode and visualize the feature column vs the target column in ascatter plot2. Repeat steps 1-3 from Task 2 on this data readnumeric-prediction-data.table Make datanon-linear Table Reader

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Created by: maarit.widmann
Created at: 2022-03-15
On NodePit since: 2023-09-25
Last update: 2024-11-23
Created with KNIME version: v4.5.2
Tags: educationL4-ML

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