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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? .151How much in percentage terms? 15.1% Read AB_NYC_2019Node 100Node 99Node 102Node 103 InteractiveData Cleaning File Reader Random Forest Learner(Regression) Replace 0 price byneighborhood average Partitioning Numeric Scorer Random Forest Predictor(Regression) 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? .151How much in percentage terms? 15.1% Read AB_NYC_2019Node 100Node 99Node 102Node 103InteractiveData Cleaning File Reader Random Forest Learner(Regression) Replace 0 price byneighborhood average Partitioning Numeric Scorer Random Forest Predictor(Regression)

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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: 2021-06-30
Last update: 2024-04-26
Created with KNIME version: v4.3.2
Tags: ensemble modelRandom Forest

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