Chartered Financial Analyst (CFA) Practice Exam Level 2 - 2025 Free CFA Level 2 Practice Questions and Study Guide

Using unsupervised learning implies that the algorithm operates on unlabeled data to identify patterns and structures within the dataset. Unlike supervised learning, which relies on labeled inputs to predict outcomes, unsupervised learning enables the identification of hidden relationships or groupings without prior knowledge of the outputs.

In unsupervised learning, techniques such as clustering and dimensionality reduction allow the algorithm to explore the data independently, leading to insights such as discovering natural groupings within the data or identifying anomalies. This exploratory approach is particularly useful in scenarios where labeling data is impractical or too costly.

The other options are not accurate representations of unsupervised learning. The need for labeled data is a characteristic of supervised learning, and while it's true that unsupervised algorithms can struggle with generalization, this is not inherently related to unsupervised learning itself. Lastly, there's no basis for claiming that unsupervised learning is always more effective than supervised learning, as their effectiveness typically depends on the specific problem being addressed and the nature of the data available.