Table of Contents

Data mining algorithms have been used with many kinds of data and for many purposes. Some have been used to improve on the work and analysis of others. An example of an updated form of machine learning algorithm is the Random Forests algorithm. Random Forests is basically an update of the decision tree algorithm. By applying the same logic of decision trees with a different methodology, Random Forests can achieve results that better fit the goals of the data mining operator.

**Random Forests Algorithm **Defined

Random Forests is built off of the framework of the decision tree algorithm and has similar uses and goals. The main differences between the true are complexity and accuracy. A decision tree algorithm is one that studies trees which are based on the potential outcomes of a particular decision and the probability that each outcome may occur. Decision trees algorithms study those trees and the resulting probabilities in order to predict future outcomes and study relationships.

These decision trees have been taken out to hundreds and thousands of decision points. The trees stray far from their initial, predictable outcomes. Researchers discovered that the decision tree algorithm started to vary heavily as the decision trees grew larger. This variance resulted from their expansion beyond a single data point. More variance means a poor data set that the data mining algorithm works from. This inverse relationship is problematic since data mining algorithms thrive on larger and larger amounts of data. A system that works more poorly as more data is added becomes an ineffective data mining system.

Random Forests is designed differently to reduce this variance in the field of data mining while still retaining many of the positives of decision trees. It is an algorithm designed to create a number of different decision trees and then study those trees at random using a rigorous methodology. The Random Forest does not simply keep each decision tree expanding past its roots. Rather, it analyzes more limited decision trees to figure out patterns and relationships between the data being displayed. The algorithm then takes this information and maps it onto several different possible outputs. In effect, the algorithm achieves a similar array of results as a decision tree algorithm by taking a different approach.

**Random Forests Algorithm **Types

The Random Forests algorithm has to be built upon a machine learning architecture like any other algorithm. This architecture has to be able to process the algorithm and respond to the changes needed for machine learning. The most popular example of these architectures is the artificial neural network. Artificial neural networks are based on the learning apparatus of the human brain. They utilize a number of nodes and connections between those nodes to convert input into output using algorithms. The nodes can be weighted and then weightings may change as the process of machine learning commences. In the case of Random Forests, each node may be a part of the equation that makes sense of the decision trees being studied.

There are two forms of learning that are often associated with Random Forests. One of these is supervised learning. Supervised learning involves a machine learning algorithm that is working to replicate an example set. The algorithm tries again and again while tweaking different parts of its system in order to reach an output within a margin of error from the example. For Random Forests, the eventual goal would be a particular series of classifications or a regression line. The supervised learning system would then process data again and again until the Random Forests algorithm drew the regression line or created the correct set of categories.

Unsupervised learning can also be performed with this algorithm. Instead of using an example set, unsupervised learning works from an initial set of guidelines. The results of unsupervised learning may be more varied than supervised learning and produce patterns that the operator would not have originally identified. In Random Forests, the unsupervised learning approach allows the algorithm to draw its own categories and analyze decision trees at different places and in different ways. The result is a trend line or set of categories that may have been far from the original expectations of the operator.

**Random Forests Algorithm **Uses

The two primary uses of the Random Forests algorithm are classification and regression. Classification involves finding the mode of the decision trees being analyzed. The result of this effort is that a massive subset of information can be placed into a coherent number of categories. Classification through random forests makes sense of data, reveals patterns, and readies that data for other future uses.

Regression is another possibility for a Random Forests algorithm. This practice involves drawing a line of the means of different data points. A regression line helps identify the trend of data and eventually use that data to make predictions. Both of these goals can be accomplished with a certain amount of clear visualization. Random Forests builds upon the work of the decision trees algorithm. Decision trees are some of the most clearly visible algorithmic products imaginable. More visualization helps users understand the work that a Random Forests algorithm is performing.

## Thoughts on **Random Forests Algorithm**

** **Random Forests is not necessarily better than the decision trees algorithm in all scenarios. There are many decisions in machine learning algorithms that only require small decision trees in order to function properly. The more complex Random Forests are not always more applicable. However, Random Forests expand the number of scenarios where these algorithms are applicable. As a result, they should be considered and used by all serious data mining operators.