How Random Forest Algorithm Works in Machine Learning?

Random forest is a supervised machine learning algorithm which can be used in both Classification and Regression problems in Machine Learning. This simple yet versatile algorithm produces good results even without hyper-parameter tuning.

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Random forest is one of the most popular algorithms based on the concept of ensemble learning. It improves the result of complex problems by combining multiple learning models. The algorithm builds multiple decision trees and combines them to produce more accurate and stable results. The more the number of trees in the forest, the more accurate is the result.

Why use the random forest algorithm?

One big reason to use random forest is that this algorithm can be used for both classification and regression problems. These are the two categories, which form the majority of current machine learning systems.

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Besides, this algorithm requires less training time as compared to other algorithms. Since it combines multiple decision trees, it produces more accurate results even with large datasets. By using an algorithms regressor, it allows us to deal with regression problems as well.

Read: Decision Tree Interview Questions

How do the random forest algorithms work?

Random forest algorithm builds a forest in the form of an ensemble of decision trees which adds more randomness while growing the trees. While splitting a node, the algorithm searches for the best features from the random subset of features which adds more diversity, thereby resulting in a better model.

Therefore, for splitting a node, only a random subset of the features is taken into consideration. Instead of searching for the best possible threshold, we can also use random thresholds for each feature to build more random trees.

Features of random forest algorithm

One of the best features of random forest is its simplicity in measuring the relative importance of each feature in the prediction. The more number of features one has, the more likely the model will suffer from overfitting. One can decide which feature is not contributing to the prediction process and therefore, should be dropped by merely looking at the feature’s importance.

Another great feature of the algorithm is versatility. Random forest can be used for both regression and classification tasks. Besides, viewing the relative importance that the algorithm assigns to the input features is also very easy.

Convenience is another feature of the Random Forest algorithm since it often produces a great prediction result by using the default hyperparameters. Besides, it is also very simple to understand the hyperparameters; there are not that many of them. 

Also Read: Types of Classification Algorithm

Limitation of random forest algorithm

To get a more accurate prediction, one requires more trees. However, more trees slow down the model. This is one of the drawbacks of the random forest algorithm. Although they can be trained fast, these algorithms are quite slow in creating predictions. Due to a large number of trees, it becomes slow and ineffective in predicting real-time results. 

There are times and situations where run-time performance is more important, and therefore other approaches are preferred over the Random Forest Algorithm.

Besides, random forest is a predictive modelling tool and not a descriptive tool. So, in cases where a description of the relationships of the data is required, other approaches are preferred over the random forest algorithm.

Where can random forest algorithms be applied?

Random forest algorithms can mainly be used in the following four sectors:

1. Banking industry to identify the risks associated with loans.
2. Marketing industry to identify market trends.
3. Pharmacy or medicinal drugs industry to identify disease trends and risks of the disease.
4. This algorithm can identify areas of similar land use.

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Conclusion

The simplicity, versatility and convenience of the random forest algorithm make it one of the most popular algorithms to train early in the model development process. This is also a great choice if you are looking to develop a model quickly. Moreover, it gives good indications of the importance it assigns to the features.

It is difficult to beat the performance of random forests. Although there will always be models which can perform better and can handle different feature types, these can usually take more time to develop.

To summarize, the random forest algorithm is a simple, versatile and quick tool with some limitations.

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