Random Forest in R – Random Forest Package and Function in R

1. Objective

In this tutorial, we will be learning about Random Forest in R. Moreover, we will cover terminologies related to R Random Forests. Along with this, we will study Random Forest classifiers and functions. Also, we will look at Ensemble Models in R.

So, let’s start Random forest in R Tutorial.

Random Forest in R - Random Forest Package and function in R

Random Forest in R – Random Forest Package and function in R

2. Introduction to Random Forest in R

What are Random Forests?
Ensemble technique called Bagging is like Random Forests. The idea behind this technique is to decorrelate the several trees. It generates on the different bootstrapped samples from training Data. And then we reduce the Variance in the Trees by averaging them. Hence, in this approach, it creates a large number of decision trees in R.
We use the R package “randomForest” to create random forests.
What is Decision Tree in R?
We can consider R as very simple and easy. Along with this, it is accountable as well as having understandable Modelling techniques. Yet, a major drawback in them is that they have a poor predictive performance and poor Generalization on Test Set.

3. What is Ensemble Learning in R?

It is a type of Supervised Learning Technique. The basic idea behind it is to generate many Models on a training dataset and then combining their Output Rules.
We will use to generate lots of Models by training on Training Set and at the end combine them. Hence, we can use it to improve the predictive performance of Decision Trees by reducing the variance in the Trees by averaging them called Random forest technique.

i. What are Ensemble Models in R?

It is a type of model which combine results from different models and usually better than the result from one of the individual models.
Some of the features of R Random Forests are as follows:

  • It is the type of model which runs on large databases.
  • Random Forests allows handling of thousands of input variables without variable deletion.
  • It gives very good estimates of which variables are important in the classification.

4. Random Forest Classifier

At training time, we can classify the ensemble learning method of a Random forest and thus we can operate it by constructing a multitude of decision trees.
Adele Cutler and Leo Breiman developed it. Here the combination of two different methods is done – Leo’s bagging idea and the random selection of features introduced by Tin Kan Ho. He also proposed Random Decision forest in the year 1995.

R Quiz

5.Functions of Random Forest in R

If the number of cases in the training set is N, and the sample N case is at random, each tree will grow. Thus, this sample will be the training set for growing the tree. If there are M input variables, we specify a number m<<M such that at each node, m variables are selected at random out of the M. The value of m is constant during the forest growing and hence, each tree grows to the largest extent possible.

So, this was all in Random Forest in R. Hope you like our explanation.

6. Conclusion

Hence, we have studied Random Forest in R. Alos, we learned different features and functionalities of R Random forests. Along with this, we have learned a random forest classifier. The random forest itself is a technique which includes many terminologies related to it. Still, if you have any query regarding Random Forest in R, ask in the comment tab.

Reference for R 

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