Which algorithm is commonly used for classification tasks in machine learning?

Decision Trees are a widely used algorithm for classification tasks in machine learning. They work by splitting the data into subsets based on the feature values, which helps in creating a model that predicts the class labels of the data. The decision tree structure makes it easy to visualize the decision-making process, as it resembles a flowchart where each internal node represents a test on a feature, each branch represents the outcome of that test, and each leaf node represents a class label.

The strength of Decision Trees lies in their ability to handle both numerical and categorical data, and they can model complex relationships without needing extensive data preprocessing. Additionally, they are intuitive and require little effort to interpret, which makes them valuable for providing insights into the data.

In contrast, Linear Regression is generally used for regression tasks, where the goal is to predict continuous outcomes rather than class labels. K-means Clustering is an unsupervised learning algorithm aimed at grouping similar data points into clusters rather than classifying them into predefined categories. Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms data to uncover the underlying structure, but it does not serve classification purposes directly. Thus, Decision Trees stand out as the appropriate choice for classification tasks among the options provided.