Random Forest: A Powerful Machine Learning Algorithm

Random forest is a supervised machine learning algorithm that can be used for both classification and regression tasks. It is a type of ensemble learning algorithm, which means that it creates a model by combining the predictions of multiple decision trees. This algorithm has gained immense popularity for its versatility and robustness. In this blog, we’ll delve into the world of Random Forests, understand the mathematics behind them, and explore real-world examples of their applications in Machine Learning.

An Ensemble of Decision Trees

Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. The ensemble approach has several advantages:

• Reduced Overfitting: Combining multiple trees helps mitigate overfitting, a common issue with individual decision trees.
• Increased Robustness: Random Forests are less sensitive to outliers and noisy data, making them suitable for a wide range of tasks.
• Accuracy: Random forest is a very accurate machine learning algorithm. It can often achieve better accuracy than other machine learning algorithms, such as decision trees and support vector machines.
• Interpretability: Random forest is a relatively easy machine learning algorithm to…