# Statistics with R for Business Analysts – Random Forest

## Statistics with R for Business Analysts – 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.

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
...............................
...............................
```

### 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,

# 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,
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.

Random Forest Ensembles for Classification | Jupyter Notebook | Python Data Science for beginners

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