Miscellaneous Regression Algorithms

A diverse spectrum of regression algorithms is examined, including decision tree, random forest, k -nearest neighbors (KNN), gradient boosting, and Gaussian process regression (GPR) algorithms. A decision tree is a supervised learning algorithm that works by recursively splitting the data based on feature values and creating branches that lead to leaf nodes representing predictions. Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy and reduce overfitting. KNN is a simple, instance-based learning algorithm that classifies or predicts a data point based on the majority class or average value of its KNN . Gradient boosting is a boosting algorithm that combines multiple weak learners (typically decision trees) sequentially to create a strong learner. GPR employs a Bayesian approach, modeling the relationship between inputs and outputs as a Gaussian process—a probability distribution over functions. A comparison is made between these regression algorithms.