Understanding Random Forest Classification and Building a Model in Python

Learn how the random forest algorithm works for the classification task.

Random forest is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use the algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. The random forest creates decision trees on randomly selected data samples, gets a prediction from each tree, and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.

The random forest has a variety of applications such as recommendation engines, image classification, and feature selection. It can be used to classify loyal loan applicants, identify fraudulent activity, and predict diseases. It lies at the base of the Boruta algorithm, which selects important features in a dataset.

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In this tutorial, you are going to learn about all of the following:

• Random forest algorithm
• How does the random forest classifier work?
• Finding important features
• Comparison between random forest and decision trees
• Building a classifier in scikit-learn
• Finding important features with…