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# R - Random Forest

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.

install.packages("randomForest)

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 −

randomForest(formula, data)

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.

# Load the party package. It will automatically load other # required packages. library(party) # Print some records from data set readingSkills. print(head(readingSkills))

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

nativeSpeaker age shoeSize score 1 yes 5 24.83189 32.29385 2 yes 6 25.95238 36.63105 3 no 11 30.42170 49.60593 4 yes 7 28.66450 40.28456 5 yes 11 31.88207 55.46085 6 yes 10 30.07843 52.83124 Loading required package: methods Loading required package: grid ............................... ...............................

### Example

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

# Load the party package. It will automatically load other # required packages. library(party) library(randomForest) # Create the forest. output.forest <- randomForest(nativeSpeaker ~ age + shoeSize + score, data = readingSkills) # View the forest results. print(output.forest) # Importance of each predictor. print(importance(fit,type = 2))

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

Call: randomForest(formula = nativeSpeaker ~ age + shoeSize + score, data = readingSkills) Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 1 OOB estimate of error rate: 1% Confusion matrix: no yes class.error no 99 1 0.01 yes 1 99 0.01 MeanDecreaseGini age 13.95406 shoeSize 18.91006 score 56.73051

### Conclusion

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.