Which algorithm is often used for multi-class classification problems?

Multi-class classification problems involve categorizing data points into multiple classes rather than just two. Random Forest is particularly suited for this type of task because it is an ensemble learning method that builds multiple decision trees during training and merges their outputs to improve accuracy and control overfitting. Each tree in the random forest can handle classification problems, and they collectively provide a robust way to predict more than two classes.

While other algorithms like Logistic Regression can be extended to handle multi-class scenarios, they typically require methods such as one-vs-all or softmax regression for proper functioning beyond binary classifications. K-Means Clustering is primarily a clustering algorithm and not suited for classification tasks, and Linear Regression is intended for regression tasks, focusing on predicting continuous outcomes rather than class labels. Therefore, Random Forest stands out as a highly effective choice for multi-class classification, providing both flexibility and strong predictive performance.