Random Forest Algorithm in Machine Learning
Machine learning, a fascinating blend of computer science and statistics, has witnessed incredible progress, with one standout algorithm being the Random Forest. Random forests or Random Decision Trees is a collaborative team of decision trees that work together to provide a single output. Originating in 2001 through Leo Breiman, Random Forest has become a cornerstone for machine learning enthusiasts. In this article, we will explore the fundamentals and implementation of Random Forest Algorithm.
What is the Random Forest Algorithm?
Random Forest algorithm is a powerful tree learning technique in Machine Learning. It works by creating a number of Decision Trees during the training phase. Each tree is constructed using a random subset of the data set to measure a random subset of features in each partition. This randomness introduces variability among individual trees, reducing the risk of overfitting and improving overall prediction performance. In prediction, the algorithm aggregates the results of all trees, either by voting (for classification tasks) or by averaging (for regression tasks) This collaborative decision-making process, supported by multiple trees with their insights, provides an example stable and precise results. Random forests are widely used for classification and regression functions, which are known for their ability to handle complex data, reduce overfitting, and provide reliable forecasts in different environments.