Random Forest - Features and Advantages

Features and Advantages

The advantages of random forest are:

  • It is one of the most accurate learning algorithms available. For many data sets, it produces a highly accurate classifier.
  • It runs efficiently on large databases.
  • It can handle thousands of input variables without variable deletion.
  • It gives estimates of what variables are important in the classification.
  • It generates an internal unbiased estimate of the generalization error as the forest building progresses.
  • It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing.
  • It has methods for balancing error in class population unbalanced data sets.
  • Prototypes are computed that give information about the relation between the variables and the classification.
  • It computes proximities between pairs of cases that can be used in clustering, locating outliers, or (by scaling) give interesting views of the data.
  • The capabilities of the above can be extended to unlabeled data, leading to unsupervised clustering, data views and outlier detection.
  • It offers an experimental method for detecting variable interactions.

Read more about this topic:  Random Forest

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