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07 Random Forest with write and read of RF model

07 Random Forest

Exercise for training a Random Forest model.

Build a Random Forest (Regression) model on a training set. Apply it to a test set, and evaluate the model's performance with numeric scoring metrics.


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 metricsUnzipCSV exampleDataUnzzipingCSV Dataresult InteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Model toBinary Object Binary Objectto Model Table Writer Table Reader Decompress Files InteractiveTable (local) 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 metricsUnzipCSV exampleDataUnzzipingCSV DataresultInteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Model toBinary Object Binary Objectto Model Table Writer Table Reader Decompress Files InteractiveTable (local)

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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: 2022-05-19
Last update: 2024-12-17
Created with KNIME version: v4.5.2
Tags: ensemble modelRandom Forest

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