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9. Random Forest Regression

Simple Model Training for Random Forest RegressionThis workflow demonstrates how a Random Forest Model is built and applied to new data.Task Predict the number of mosquitoes found on the basis of species and trap type. Data ReadingRead the new file 'westnilevirus.csv'created after data preprocessing. It contains:1. Trap Type2. Number of Mosquitoes3. Species4. Test Results Data PartitioningCreate two separate partitionsfrom original data set1. training set (80%) 2. test set (20%). Train a ModelThis node builds a random forestregression model. Most Learnernodes output a PMML model (bluesquare output port). Apply the ModelPredictor nodes apply a specificmodel to a data set and append themodel predictions. Score the ModelProvide with the Accuracy ofRegression in the form of RSquare training set test set Interactive TableDisplay table of the entire data Reading westnilevirus.csvRandom drawing 80% upper port20% lower portShow entire data as tableTarget Class= Number of MosquitoesApply the trained modelfor predictionRegression Accuracy File Reader Partitioning InteractiveTable (local) Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Simple Model Training for Random Forest RegressionThis workflow demonstrates how a Random Forest Model is built and applied to new data.Task Predict the number of mosquitoes found on the basis of species and trap type. Data ReadingRead the new file 'westnilevirus.csv'created after data preprocessing. It contains:1. Trap Type2. Number of Mosquitoes3. Species4. Test Results Data PartitioningCreate two separate partitionsfrom original data set1. training set (80%) 2. test set (20%). Train a ModelThis node builds a random forestregression model. Most Learnernodes output a PMML model (bluesquare output port). Apply the ModelPredictor nodes apply a specificmodel to a data set and append themodel predictions. Score the ModelProvide with the Accuracy ofRegression in the form of RSquare training set test set Interactive TableDisplay table of the entire data Reading westnilevirus.csvRandom drawing 80% upper port20% lower portShow entire data as tableTarget Class= Number of MosquitoesApply the trained modelfor predictionRegression Accuracy File Reader Partitioning InteractiveTable (local) Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer

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Created by: Admin
Created at: 2020-05-01
On NodePit since: 2020-05-07
Last update: 2024-11-23
Created with KNIME version: v4.1.2

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