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RF_​SVM (1)

Loads the cleaned crash dataset
CSV Reader
Splits data into training and test folds
X-Partitioner
Balances class distribution in training data
Equal Size Sampling
Trains the Random Forest classification model
Random Forest Learner
Applies the model to generate predictions
Random Forest Predictor
Combines results from all folds
X-Aggregator
Removes leakage variables and keeps relevant predictors.
Column Filter
Combines results from all folds
X-Aggregator
Balances class distribution in the training data.
Equal Size Sampling
Trains the SVM model to predict injury severity.
SVM Learner
Applies the same scaling to test data using training parameters.
Normalizer (Apply)
Scales numeric features to improve SVM performance.
Normalizer
Splits data into training and test folds
X-Partitioner
Evaluates model performance using metrics
Scorer
Generates predictions on the test data.
SVM Predictor
Combines results from all folds
X-Aggregator
Converts categorical variables into numeric dummy variables for SVM.
One to Many
Converts categorical variables into numeric dummy variables for SVM.
One to Many
Loads the cleaned crash dataset
CSV Reader
Balances class distribution in the training data.
Equal Size Sampling
Splits data into training and test folds
X-Partitioner
ROC Curve
Trains the SVM model to predict injury severity.
SVM Learner
Loads the cleaned crash dataset
CSV Reader
ROC Curve
Scales numeric features to improve SVM performance.
Normalizer
ROC Curve
Generates predictions on the test data.
SVM Predictor
Evaluates model performance using metrics
Scorer
Applies the same scaling to test data using training parameters.
Normalizer (Apply)
Evaluates model performance using metrics
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: tyler
Created at: 2026-04-30
On NodePit since: 2026-05-02
Last update: 2026-05-02
Created with KNIME version: v5.8.3

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