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.