Learning Model Category

A Learning Model Category is a model category for learning models.

• Context:
  • It can (typically) include models that learn from data to make predictions or decisions.
  • It can (often) be classified into supervised, unsupervised, and reinforcement learning models.
  • It can range from simple linear models to complex deep learning architectures.
  • It can (commonly) be used in a variety of fields such as healthcare, finance, and technology.
  • It can (occasionally) be adapted to work with different types of data including text, images, and time series.
  • ...
• Example(s):
  • DNN Model.
  • Decision Tree Model.
  • SVM Model.
  • ...
• Counter-Example(s):
  • Heuristic Algorithms, which do not learn from data but follow predefined rules.
  • Rule-Based Systems, which rely on explicit instructions rather than data-driven learning.
• See: Supervised Learning Task, Machine Learning Model, Decision Tree Metamodel, Neural Network Metamodel, Statistical Metamodel, CRF, HMM.

---

---