Random forest is a flexible, easy-to-use machine learning algorithm that produces a great result most of the time, even without hyper-parameter tuning.

It is also one of the most used algorithms because of its simplicity and diversity (it can be used for both classification and regression tasks).

Random forest builds multiple decision trees and merges them to get a more accurate and stable prediction.

Random Forest: Ensemble tree-based algorithm

It is an ensemble tree-based learning algorithm. The Random Forest Classifier is a set of decision trees from a randomly selected subset of the training set. It aggregates the votes from different decision trees to decide the final class of the test object.

You'll learn about ensemble models in detail in the next unit.

Applications

• The random forest algorithm is used in many fields, like banking, the stock market, medicine, and e-commerce.
• In finance, for example, it is used to detect customers more likely to repay their debt on time or use a bank's services more frequently. In this domain, it is also used to detect fraudsters out to scam the bank. In trading, the algorithm can be used to determine a stock's future behavior.
• In the healthcare domain, it is used to identify the correct combination of components in medicine and to analyze a patient's medical history to identify diseases.
• Random forest is used in e-commerce to determine whether a customer will like the product.

Resources on Random Forest Classification

• Implementation of Random Forest Classification: https://www.datacamp.com/community/tutorials/random-forests-classifier-python
• Notebook:
  https://aiplanet.com/notebooks/864/manish\_kc\_06/random-forest

Slide Download Link

You can download the slides for this topic from here.