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Sampling Strategies Comparison

Sampling Imbalance SMOTE

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Experiment with: - simple random sampling - stratified random sampling (Partitioning node) - undersampling (Equal Size Sampling node) - oversampling (Bootstrap Sampling node and SMOTE node) The workflow draws on the kaggle Stroke Prediction Dataset that represents 5110 rows with 11 clinical features such as body mass index, smoking status, age, gender, and glucose level. The task is to predict stroke (yes/no), which is a classification problem. We chose to build a Random Forest model.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.2
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

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    KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

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    KNIME Machine Learning Interpretability Extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

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