Sampling Strategies Comparison_​OK

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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.

Nodes

• Partitioning5 ×
• Random Forest Learner5 ×
• Random Forest Predictor5 ×
• Binary Classification Inspector1 ×
• Bootstrap Sampling1 ×
• Show all 13 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME Machine Learning Interpretability Extension

Links

• No links available