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07 Random Forest - Solution

07 Random Forest - Solution
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 priceR2 and error metrics InteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average 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 priceR2 and error metricsInteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average

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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: 2021-07-21
Last update: 2024-11-04
Created with KNIME version: v4.4.0
Tags: ensemble modelRandom Forest

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