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rf-model-importance-analysis

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Use when you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.

When to use

• train a two-class random forest model
• rank variable importance
• generate reproducible error and importance plots
• plot-only rerender

When not to use

• regression tasks
• multi-class classification
• missing-value imputation
• preprocessing
• remote data fetching
• time-series modeling
• normalization
• batch correction

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