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Comment
. 2018 Feb 20;115(8):1690-1692.
doi: 10.1073/pnas.1800256115. Epub 2018 Feb 12.

Classification and interaction in random forests

Danielle Denisko  1   2 , Michael M Hoffman  3   2   4
Affiliations
Comment

Classification and interaction in random forests

Danielle Denisko et al. Proc Natl Acad Sci U S A. .
No abstract available

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Individual decision trees vote for class outcome in a toy example random forest. (A) This input dataset characterizes three samples, in which five features (x1, x2, x3, x4, and x5) describe each sample. (B) A decision tree consists of branches that fork at decision points. Each decision point has a rule that assigns a sample to one branch or another depending on a feature value. The branches terminate in leaves belonging to either the red class or the yellow class. This decision tree classifies sample 1 to the red class. (C) Another decision tree, with different rules at each decision point. This tree also classifies sample 1 to the red class. (D) A random forest combines votes from its constituent decision trees, leading to a final class prediction. (E) The final output prediction is again the red class.
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References

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