Random Forest Algorithm

Another state-of-the-art algorithm in practice in the machine learning community is the Random forest algorithm. In this blog, let us understand how the algorithm works.

Random forest is a supervised, ensemble learning algorithm that can be used for classification and regression. Ensemble learning is a method where multiple machine learning algorithms are used at the same time. It works by combining the learnings of multiple weak models, resulting in a much more effective and trustworthy model.

Random forests are generally made up of multiple Decision Tree algorithms. A Decision Tree asks a “question” in the simplest possible terms and then classifies the column based on their answer.?Here is a simple example of what a decision tree looks like.?

The above example is a simple way of predicting what you can do today given the factors like weather, work, etc.

But in most machine learning scenarios, decision trees are said to overfit the data, and random forest overcomes this problem by following the below steps.

1. Pick N random records from the dataset.
2. Build a decision tree based on these N records.
3. Choose the number of trees you want in your algorithm and repeat steps 1 and 2.
4. In the case of a regression problem, each tree in the forest predicts a value for Y (output) for a new record. The final deal can be calculated by taking the average of all the values expected from all the forest trees. Or, in the case of a classification problem, each tree in the forest predicts the category to which the new record belongs. Finally, the new form is assigned to the class that wins the majority vote.

There are numerous applications for Random forests, and the most specific areas include predicting the stock market, detecting banking fraud, etc.

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