Which algorithm is commonly used for classification tasks?

The algorithm that is commonly used for classification tasks is Decision Trees. Decision Trees work by splitting the dataset into branches based on the values of the features, leading to decision nodes that ultimately result in class labels. This method allows for both linear and non-linear decision boundaries, making it versatile for various types of classification problems. The tree structure visually represents the decision-making process, which helps in interpretability — a key advantage when communicating findings.

In the context of other provided algorithms, Linear Regression is primarily focused on predicting continuous outcomes rather than classification. K-Means Clustering is an unsupervised learning algorithm used for grouping data into clusters without pre-defined classes, which does not apply to classification scenarios. Principal Component Analysis (PCA) is a dimensionality reduction technique used for reducing the number of features in data while retaining the most important information, and is not used directly for classification tasks.