What is a common algorithm used for classification tasks in machine learning?

Logistic Regression is a widely used algorithm for classification tasks in machine learning due to its effectiveness in estimating the probability that a given input belongs to a particular category. This algorithm is ideal for binary classification problems where the outcome can take on one of two possible values. It works by applying the logistic function to a linear combination of input features, producing a value between 0 and 1, which can then be interpreted as a probability.

Additionally, the simplicity of Logistic Regression makes it a strong choice for many real-world scenarios. It allows for easy interpretation of the coefficients, providing insights into how input features affect the likelihood of a specific outcome. Due to its foundational role in statistics and machine learning, Logistic Regression often serves as a benchmark model against which more complex algorithms are compared.

In contrast, other algorithms listed, while useful for various tasks, are not primarily focused on classification in the way that Logistic Regression is. For example, k-Nearest Neighbors is also commonly used for classification, but it relies heavily on the distance between points and may not perform as well with high-dimensional data. Linear Regression, on the other hand, is meant for regression tasks where the output is continuous, not categorical. Decision Trees can be utilized for classification, but they have a