IBM Data Science Practice Test

Which machine learning method is characterized by experimenting with actions to maximize a reward?

1. Unsupervised learning

2. Reinforcement learning

3. Supervised learning

4. Regression analysis

The correct answer is: Reinforcement learning

Reinforcement learning is characterized by the concept of experimenting with actions in order to maximize a reward. This method is inspired by behavioral psychology, where an agent learns to make decisions by taking actions in an environment and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that dictates the best actions to take in various situations to achieve the highest cumulative reward over time. In reinforcement learning, the agent explores different actions and observes the consequences, which helps it refine its policy based on the outcomes. This trial-and-error approach allows the agent to improve its performance as it gains more experience, ultimately learning to act in a way that maximizes the reward. The other methods mentioned, such as unsupervised learning and supervised learning, focus on different types of learning tasks. Unsupervised learning deals with finding patterns or groupings in data without labeled outputs, while supervised learning involves training models on labeled datasets to predict outcomes. Regression analysis is a specific statistical method used to understand relationships between variables and predict continuous outcomes rather than experimenting for rewards.