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Home Support FAQs RandomForests What are the advantages of RandomForests?

What are the advantages of RandomForests?

  • Automatic predictor selection from any number of candidates
    • The analyst does not need to do any variable selection or data reduction.
    • The best predictors are automatically identified.
      • Ability to handle data without preprocessing
        • Data do not need to be rescaled, transformed, or modified.
        • resistant to outliers
        • automatically handles missing values
      • Resistance to over training
        • Numerous trees are generated based on two forms of randomization.
        • Growing a large number of RandomForests trees does not create a risk of overfitting.
        • Each tree is an independent, random experiment.
      • Self-testing using “out-of-bag” data
        • Self-testing is based on an extension of cross-validation.
        • Self-tests provide highly reliable assessments of the model.
      • Cluster identification
        • can be used to generate tree-based clusters
        • Predictor variables defining clusters are chosen automatically.
      • Visualization
        • RandomForests offers graphics that yield new insights into data.
How does RandomForests work? What are RandomForests’ strengths?
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