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.

R – Survival Analysis

Survival analysis deals with predicting the time when a specific event is going to occur. It is also known as failure time analysis or analysis of time to death. For example predicting the number of days a person with cancer will survive or predicting the time when a mechanical system is going to fail.
The R package named survival is used to carry out survival analysis. This package contains the function Surv() which takes the input data as a R formula and creates a survival object among the chosen variables for analysis. Then we use the function survfit() to create a plot for the analysis.

Install Package

install.packages(“survival”)
Syntax
The basic syntax for creating survival analysis in R is −
Surv(time,event)
survfit(formula)
Following is the description of the parameters used −
• time is the follow up time until the event occurs.
• event indicates the status of occurrence of the expected event.
• formula is the relationship between the predictor variables.
Example
We will consider the data set named “pbc” present in the survival packages installed above. It describes the survival data points about people affected with primary biliary cirrhosis (PBC) of the liver. Among the many columns present in the data set we are primarily concerned with the fields “time” and “status”. Time represents the number of days between registration of the patient and earlier of the event between the patient receiving a liver transplant or death of the patient.
# Load the library.
library(“survival”)

# Print first few rows.
print(head(pbc))
When we execute the above code, it produces the following result and chart −
id time status trt age sex ascites hepato spiders edema bili chol
1 1 400 2 1 58.76523 f 1 1 1 1.0 14.5 261
2 2 4500 0 1 56.44627 f 0 1 1 0.0 1.1 302
3 3 1012 2 1 70.07255 m 0 0 0 0.5 1.4 176
4 4 1925 2 1 54.74059 f 0 1 1 0.5 1.8 244
5 5 1504 1 2 38.10541 f 0 1 1 0.0 3.4 279
6 6 2503 2 2 66.25873 f 0 1 0 0.0 0.8 248
albumin copper alk.phos ast trig platelet protime stage
1 2.60 156 1718.0 137.95 172 190 12.2 4
2 4.14 54 7394.8 113.52 88 221 10.6 3
3 3.48 210 516.0 96.10 55 151 12.0 4
4 2.54 64 6121.8 60.63 92 183 10.3 4
5 3.53 143 671.0 113.15 72 136 10.9 3
6 3.98 50 944.0 93.00 63 NA 11.0 3

From the above data we are considering time and status for our analysis.
Applying Surv() and survfit() Function
Now we proceed to apply the Surv() function to the above data set and create a plot that will show the trend.

# Load the library.
library("survival")

# Create the survival object. 
survfit(Surv(pbc$time,pbc$status == 2)~1)

# Give the chart file a name.
png(file = "survival.png")

# Plot the graph. 
plot(survfit(Surv(pbc$time,pbc$status == 2)~1))

# Save the file.
dev.off()
When we execute the above code, it produces the following result and chart −
Call: survfit(formula = Surv(pbc$time, pbc$status == 2) ~ 1)

n events median 0.95LCL 0.95UCL 
418 161 3395 3090 3853

The trend in the above graph helps us predicting the probability of survival at the end of a certain number of days.
So, this brings us to the end of blog. This Tecklearn ‘Random Forest and Survival Analysis in R’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Logistic Regression
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Classification Techniques
• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
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• Decision Tree in R
• Information Gain
• Gini Index
• Pruning
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