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07 Random Forest

07 Random Forest
Exercise: Random Forest (Regression)Build a Random Forest model to predict the price of a room.1.1) Execute the workflow below. It reads and preprocesses the Airbnb data.1.2) Partition the data into a training set (70%) and a test set (30%). Applyrandom sampling.1.3) Train a Random Forest (Regression) model to predict the price column1.4) Apply the model to the test set1.5) Evaluate the model's performance with the Numeric Scorer node1.6) How much in average does the prediction deviate from the actual roomprice per night? How much in percentage terms? Read AB_NYC_2019datapredict pricetop: training set (70%)bottom: test set (30%)R^2 and errormetrics InteractiveData Cleaning CSV Reader Replace 0 price byneighborhood average Random Forest Learner(Regression) Random Forest Predictor(Regression) Partitioning Numeric Scorer Exercise: Random Forest (Regression)Build a Random Forest model to predict the price of a room.1.1) Execute the workflow below. It reads and preprocesses the Airbnb data.1.2) Partition the data into a training set (70%) and a test set (30%). Applyrandom sampling.1.3) Train a Random Forest (Regression) model to predict the price column1.4) Apply the model to the test set1.5) Evaluate the model's performance with the Numeric Scorer node1.6) How much in average does the prediction deviate from the actual roomprice per night? How much in percentage terms? Read AB_NYC_2019datapredict pricetop: training set (70%)bottom: test set (30%)R^2 and errormetricsInteractiveData Cleaning CSV Reader Replace 0 price byneighborhood average Random Forest Learner(Regression) Random Forest Predictor(Regression) Partitioning Numeric Scorer

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Maarit Laukkanen
Created at: 2020-10-27
On NodePit since: 2022-03-11
Last update: 2024-12-17
Created with KNIME version: v4.5.1
Tags: ensemble modelRandom Forest

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