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In a random collection of data from independent sources, it is generally observed that the distribution of data is normal. Which means, on plotting a graph with the value of the variable in the horizontal axis and the count of the values in the vertical axis we get a bell shape curve.

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In the random forest approach, a large number of decision trees are created. Every observation is fed into every decision tree. The most common outcome for each observation is used as the final output. A new observation is fed into all the trees and taking a majority vote for each classification model.

An error estimate is made for the cases which were not used while building the tree. That is called an OOB (Out-of-bag) error estimate which is mentioned as a percentage.

The R package 'randomForest' is used to create random forests.

Install R Package

Use the below command in R console to install the package. You also have to install the dependent packages if any.

The package 'randomForest' has the function randomForest() which is used to create and analyze random forests.

Syntax

The basic syntax for creating a random forest in R is −

Following is the description of the parameters used −

• formula is a formula describing the predictor and response variables.

• data is the name of the data set used.

Input Data

We will use the R in-built data set named readingSkills to create a decision tree. It describes the score of someone's readingSkills if we know the variables 'age','shoesize','score' and whether the person is a native speaker.

Here is the sample data.

When we execute the above code, it produces the following result and chart −

Example

We will use the randomForest() function to create the decision tree and see it's graph.

When we execute the above code, it produces the following result −

Conclusion

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From the random forest shown above we can conclude that the shoesize and score are the important factors deciding if someone is a native speaker or not. Also the model has only 1% error which means we can predict with 99% accuracy.