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

Logistic regression is indeed a widely utilized algorithm for classification tasks in machine learning. It is designed to predict the probability that a given input belongs to a particular category or class. This is accomplished by applying the logistic function, which transforms linear combinations of the input features into a value between 0 and 1, effectively representing class probabilities.

One major strength of logistic regression is its interpretability. It allows for easy understanding of the relationship between predictor variables and the response variable, with coefficients indicating the impact of each feature. Moreover, logistic regression assumes a linear relationship between the features and the log-odds of the outcome, which, under certain conditions, can provide accurate predictions.

In comparison to other algorithms listed, logistic regression operates specifically for binary classification and can be extended to multiclass problems using variations such as one-vs-all. Meanwhile, decision trees and random forests also serve classification purposes but operate with different methodologies and can handle more complex data structures, while K-means clustering is fundamentally an unsupervised algorithm used for grouping data points rather than for direct classification tasks. Thus, logistic regression holds its ground as one of the foundational methods for classification in machine learning.