In machine learning, the random forest algorithm is also known as the random forest classifier. It is a very popular classification algorithm. One of the most interesting thing about this algorithm is that it can be used as both classification and random forest regression algorithm. The RF algorithm is an algorithm for machine learning, which is a forest. We know the forest consists of a number of trees. The trees being mentioned here are decision trees. Therefore, the RF algorithm comprises a random collection or a random selection of a forest tree. It is an addition to the decision tree algorithm. So basically, what a RF algorithm does is that it creates a random sample of multiple decision trees and merges them together to obtain a more stable and accurate prediction through cross validation. In general, the more trees in the forest, the more robust would be the prediction and thus higher accuracy.
In order to completely understand the nature of the random forest algorithm, it is important that you first understand the concept of a decision tree classifier.
Decision Tree algorithm is an algorithm that can be used to solve regression as well as classification problems. The main objective of the creation of a decision tree is to build a training model or training set. This training model is used to predict the value or class of the recipient variables or categorical variables. The level of understanding of the decision trees algorithm is much easier than the other classification algorithms.
In the RF classifier, every decision tree forecasts a response for an occurrence and the endmost response is decided through voting. On contrary, in classification, the response received by majority voting of Decision Tree is the final response and in regression, the final response is the average of all the responses.
Why use Random Forest Algorithm
- Random forest algorithm can be used for both classifications and regression task.
- It provides higher accuracy through cross validation.
- Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data.
- If there are more trees, it won’t allow over-fitting trees in the model.
- It has the power to handle a large data set with higher dimensionality
How does it work
In the random forest, we grow multiple trees in a model. To classify a new object based on new attributes each tree gives a classification and we say that tree votes for that class. The forest chooses the classifications having the most votes of all the other trees in the forest based on the importance score and takes the average difference from the output of different trees. In general, RF built multiple trees and combines them together to get a more accurate result.
While creating random trees it split into different nodes or subsets. Then it searches for the best outcome from the random subsets. This results in the better model of the algorithm. Thus, in a random forest, only the random subset is taken into consideration.
To give you a clear idea about the working of a random tree, let us see an example.
Suppose we formed a thousand random trees to form the random forest to detect a ‘hand’. Each random forest will predict the different outcomes or the class for the same test features. A small subset of the forest will look at the random set of features, for example, hand or fingers. Suppose some hundred random decision trees predict some unique targets such as thumb, fingers or human. Then the votes of the finger are calculated out of a hundred random decisions and also the votes of thumb and human. If votes of the finger are higher, then the final random forest will return the finger as a predicted target. This type of voting is called majority voting. The same applies to the rest of the fingers of the hand, if the algorithm predicts the rest of the fingers to be fingers of a hand, then the high-level decision tree can vote that an image is a ‘hand’. This is why the random forest is also known as Ensemble machine learning algorithm.
For training data or training test sets in machine learning, this algorithm helps in several ways and most of the applications are underway. Below we have discussed the use of this algorithm in machine learning in a few sectors.
When to use Random Forest Analysis
There are several applications where a RF analysis can be applied. We will discuss some of the sectors where random forest can be applied. We will also look closer when the random forest analysis comes into the role.
Banking Sector: The banking sector consists of most users. There are many loyal customers and also fraud customers. To determine whether the customer is a loyal or fraud, Random forest analysis comes in. With the help of a random forest algorithm in machine learning, we can easily determine whether the customer is fraud or loyal. A system uses a set of a random algorithm which identifies the fraud transactions by a series of the pattern.
Medicines: Medicines needs a complex combination of specific chemicals. Thus, to identify the great combination in the medicines, Random forest can be used. With the help of machine learning algorithm, it has become easier to detect and predict the drug sensitivity of a medicine. Also, it helps to identify the patient’s disease by analyzing the patient’s medical record.
Stock Market: Machine learning also plays role in the stock market analysis. When you want to know the behavior of the stock market, with the help of Random forest algorithm, the behavior of the stock market can be analyzed. Also, it can show the expected loss or profit which can be produced while purchasing a particular stock.
E-Commerce: When you will find it difficult to recommend or suggest what type of products your customer should see. This is where you can use a random forest algorithm. Using a machine learning system, you can suggest the products which will be more likely for a customer. Using a certain pattern and following the product’s interest of a customer, you can suggest similar products to your customers.