# Understand The Random Forest Model in Data Science

Data science is used for predicting smart solutions to modern problems nowadays by processing big data. But that processing is a very tedious job. The data produces in such cases are large, unstructured chunks of data often referred to as raw data.

This unstructured data has to be classified and separated into different clusters of data. Random forest model is a classification algorithm offered by Data Science Training that arranges data in a structured way using decision trees. It is ranked highly among other classification algorithms because of its high performance and efficiency. Let us know more about this algorithm and its working.

To dive deeper into this algorithm, we must have a pre-requisite about decision trees. A decision tree is a way of dividing a data set into different categories/classes by mapping the elements of the given data set in a tree based on decisions and at each level of the tree a question is asked which leads to the branching of the tree into several categories. For example, suppose we have a data set of 1s which are of two colors and are either underlined or not. Now, we have to make a decision tree and classify the given data set into various categories.

Given data set = ( 1 , 1 , 1 , 1 , 1 , 1 , 1 )

Decision tree –

1  1  1  1  1  1  1

1  1  1                         1  1  1  1

1  1                                   1

As you can see in the above figure that on the first level of the tree a question has been asked i.e. is 1 red? Based on this question the 1s are divided into two categories and then based on whether the 1 is underlined or not, the branching of nodes is done on level 2. So, it is a very simple yet powerful means of data classification and helps even more when the data set is huge.

When the data is large in volume then a lot of individual decision trees are made from the given data set. These decision trees have classified the data depending on their attributes and characteristics. Once the trees are made, they are brought together to form a forest of trees which has different sets of data. These trees act as a community and serve their purpose of data classification. Together, they perform very well and give far better results as compared to other models/algorithms.

These individual trees in the forest perform as an ensemble which is further used for predictive analysis and other data science operations. The outcome of this model is uncorrelated. Uncorrelated outcomes do not affect each other and as we have many trees, the accuracy of our prediction increases. There are ways to ensure that the trees don’t affect each other i.e. the trees should be uncorrelated to each other. It is done in two ways that are feature randomness and bagging.

In bagging, different trees are made by slightly changing the sample data set which is random, as the decision trees are very sensitive even to a slight change in the data set. This ensures that the trees are uncorrelated. In feature randomness, whenever we branch the decision tree, we use that property of the data set which results in the highest number of branches. If we have numerous possibilities then we predict with more accuracy using each and every value the given data set can possess.

Conclusion

The forest serves as a great deal to analysts and is widely used. Each decision tree in the forest is made by changing the data set. The change in the data set is done through random values that replace the original data set and thus create more possibilities and a way for better and accurate prediction. This article was all about the random forest model for data classification in data science.

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