It seems like you’re inquiring about a type of machine learning. There are various types of machine learning, which can generally be categorized into three main categories:

1. Supervised Learning: In this type, the model is trained on labeled data. That means, during training, the algorithm is provided with inputs and their corresponding correct outputs. The goal is to learn a function that maps inputs to the correct outputs. Examples include regression and classification algorithms like Linear Regression, Support Vector Machines, and Decision Trees.
2. Unsupervised Learning: Unlike supervised learning, unsupervised learning does not use labeled outputs in the training data. The goal of unsupervised learning is to find patterns and relationships in the data. Examples include clustering techniques like K-Means and Hierarchical Clustering.
3. Reinforcement Learning: This is a type of learning where an agent interacts with an environment to achieve a goal or maximize some notion of cumulative reward. The agent learns to make decisions by trial and error, discovering the effects of actions to arrive at the best strategy or policy. Examples include algorithms like Q-Learning and Deep Q Networks (DQN).

Within these categories, there are various techniques, algorithms, and approaches that can be applied to different problems or data sets.