IBM Data Science Practice Test

Which type of learning does not rely on labeled outcomes in its training data?

1. Supervised learning

2. Unsupervised learning

3. Reinforcement learning

4. Regression analysis

The correct answer is: Unsupervised learning

Unsupervised learning is characterized by its ability to work with data that does not have labeled outcomes. In this approach, the model identifies patterns and structures within the data without guidance from pre-existing labels or targets. It allows the algorithm to explore the inherent relationships and groupings within the dataset freely, which is particularly useful for clustering data points or reducing dimensionality. In contrast, supervised learning requires labeled outcomes to train the model, meaning each training example must have a corresponding target value. Reinforcement learning involves training an agent to make decisions by providing feedback through rewards or penalties based on its actions within an environment, which also relies indirectly on feedback mechanisms. Regression analysis, while a method used in supervised learning, specifically focuses on predicting continuous outcomes based on input features. Thus, these other types of learning fundamentally rely on some form of labeled data or feedback mechanisms, making unsupervised learning distinct.