Which algorithm is most commonly associated with supervised learning?

The algorithm most commonly associated with supervised learning is Neural Networks. This approach involves models that learn from labeled training data, where each input is paired with the correct output. Neural networks are capable of capturing complex patterns in data by adjusting their weights through backpropagation during training. They are particularly effective for tasks such as image recognition, natural language processing, and other applications requiring classification or regression based on labeled data.

In contrast, K-Means Clustering is an unsupervised learning technique that groups data into clusters without predefined labels. Principal Component Analysis serves as a dimensionality reduction technique and is also unsupervised, focusing on transforming data to highlight its most important features without reference to output labels. Q-Learning is a type of reinforcement learning algorithm focused on learning how to act optimally in a given environment based on rewards, rather than learning directly from labeled data.

The distinct hallmark of supervised learning is the reliance on labeled datasets, making Neural Networks the appropriate choice when discussing algorithms in this context.