Overview of Random Forest Algorithm
Algorithms are a set of steps followed to do a complex calculation to solve problems. Algorithms are created to solve machine learning problems. Random forest algorithm is one such algorithm used for machine learning. It is used to train the data based on the previously fed data and predict the possible outcome for the future. It is a very popular and powerful machine learning algorithm.
Understanding the Random Forest Algorithm
The random forest algorithm is based on supervised learning. It can be used for both regression and classification problems. As the name suggests Random Forest can be viewed as a collection of multiple decision trees algorithm with random sampling. This algorithm is made to eradicate the shortcomings of the Decision tree algorithm.
Random forest is a combination of Breiman’s “bagging” idea and random selection of features. The idea is to make the prediction precise by taking average or mode of the output of multiple decision trees. The greater the number of decision trees is considered the more precise output will be.
Working
To understand the working of the Random forest, first, we need to understand the working of the decision tree as the Random forest is based on decision trees.
Decision Tree
It is a simple but popular algorithm that follows a top-down approach. Each node in the decision tree represents an attribute and the leaf represents the outcome. Branches that link nodes to leaves are the decisions or the rules for prediction. The root node is the attribute that best describes the training dataset. Thus, the overall process is diagrammed into a tree-like structure.
Limitations of Decision Tree: It tends to overfit on the training dataset. Hence when used with a test or different dataset results can be different. It leads to poor decisions. Trees can be unstable as a slight change in data can lead to a completely different tree.
Random forest uses the bagging method to get the desired outcome. The concept is to apply the decision tree algorithm on the dataset but every time with different samples of training data. The output of these decision trees will be different and might be biased based on the training data fed to the algorithm. So, the final output can be taken as average or mode of the output of individual decision tree. Hence variance can be reduced. The sampling can be done with replacement. The outputs of decision trees are ranked and the one with the highest rank will be the final output of Random Forest. Thus, the obtained output will be less biased and more stable.
Importance of Random Forest Algorithm
Below are a few important listed.
- Random forest algorithm can be used for both regression and classification models of machine learning.
- It can also handle missing values in the dataset.
- Unlike the decision tree, it won’t overfit the model and can be used for categorical variables also. Random forest adds randomness to the model.
- Unlike decision trees, instead of searching the single most important feature to build a decision tree around, it searches the best feature using a random subset of features for trees.
- And then generate the output based on the most ranked output of subset decision trees.
Real-life Example
Suppose a girl named Lisa wants to start a book, so she went to one of her friends David and ask for his suggestion. He suggested Lisa a book based on the writer she had read. Similarly, she went to a few other friends for their suggestions and based on the genre, author, and publisher they suggested some books. She made a list out of that. Then she purchased a book that most of her friends had suggested.
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Assume Her friends being decision tree and genre, author, publisher, etc. being features of data. Hence Lisa going to different friends are a representation of different decision trees. Therefore, the output of the algorithm is the book that got most of the votes.
Random Forest Algorithm Applications
Some of the applications are as given below.
- Random forest algorithm is used in a lot of fields like banking, e-commerce, medicine, stock market, etc.
- In banking, it is used to determine loyal customers and fraud customers. It is used to detect which customer will be able to pay the loan back. Because in banking it is very important to issue loans only to those customers who will be able to pay it in time. Also, a random forest is used to predict if a customer is fraudulent or not. Bank’s growth depends on such type of prediction.
- In the medicinal field, the random forest is used to diagnose the disease based on patient’s past medical records.
- In the stock market, the random forest is used to identify the market and stock behavior.
- In the field of e-commerce, this algorithm is used to predict the customer’s preference based on past behavior.
Advantages
- As mentioned above it can be used for both regression and classification type of problem. It is easy to use. Overfitting of the dataset is not a problem in the random forest algorithm.
- It can be used to identify the most important feature among available features. With the use of hyperparameter often good predictions are produced and it is very simple to understand.
- The random forest has high accuracy, flexibility, and less variance.
Disadvantage
- As the number of trees increases, the algorithm becomes slow and ineffective in handling real-time scenarios.
- Random forest is more time consuming as compared to the decision tree.
- It also requires more resources for computation.
Examples: Companies use machine learning algorithms to understand their customers better and grow their business. A random forest algorithm can be used to understand the preference of the customer. It can also be used to predict the likelihood of a person buying a certain product. Suppose, given features like weight, height, color, average, fuel consumption, etc. of a vehicle, the company can predict if it will be a successful product in the market or not. It can be used to identify factors responsible for high sales.
Conclusion
The random forest algorithm is simple to use and an effective algorithm. It can predict with high accuracy and that’s why it is very popular.
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