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Scoring RandomForests models

Applying Models to New Data Occasionally users ask us how to make use of a model they have just built, and specifically, how to generate predictions from model. In this note we will discuss RandomForests models although the general ideas are relevant for any SPM generated model.

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What is RandomForests®?

RandomForests represents a newly-developed data analysis tool for data mining and predictive modeling. It generates and combines decision trees into predictive models and displays data patterns with a high degree of accuracy. The method was developed by Leo Breiman and Adele Cutler of University of California, Berkeley, and is licensed exclusively to Salford Systems.

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Quick Overview of Unsupervised Learning in Salford SPM

The SPM Salford Predictive Modeler software suite offers several tools for clustering and segmentation including CART, RandomForests, and a classical statistical module CLUSTER. In this article we illustrate the use of these tools with the well known Boston Housing data set (pertaining to 1970s housing prices and neighborhood characteristics in the greater Boston area).

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How does RandomForests work?

RandomForests is a collection of many CART® trees that are not influenced by each other when constructed. The sum of the predictions made from decision trees determines the overall prediction of the forest. Two forms of randomization occur in RandomForests, one by trees and one by node. At the tree level, randomization takes place via observations. At the node level, randomization occurs by using a randomly-selected subset of predictors. Each tree is grown to a maximal size and left unpruned. This process is repeated until a user-defined number of trees is created, a collection called a random forest. Once this is created, the predictions for each tree are used in a "voting" process. The overall prediction is determined by voting for classification and by averaging for regression.

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What are RandomForests’ strengths?

RandomForests specializes in classification and regression problems. Its strengths are spotting outliers and anomalies in data, displaying proximity clusters, predicting future outcomes, identifying important predictors, discovering data patterns, replacing missing values with imputations, and providing insightful graphics. Additionally, it can provide clustering and density estimations.

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