modellearning
Modellearning is the process of constructing predictive or descriptive models from data. It encompasses choosing appropriate algorithms, selecting input features, tuning hyperparameters, and updating models as new data becomes available. The goal is to create representations that generalize beyond the observed samples to make accurate predictions or provide insights about underlying processes.
Common model families include linear models, decision trees and ensembles, neural networks, and probabilistic or Bayesian
Typical workflow ranges from problem framing and data preprocessing to model training, validation, and deployment. Techniques
Applications span finance, healthcare, engineering, marketing, and beyond. Modellearning faces challenges including data quality and representativeness,
Related terms include model-based learning in reinforcement learning, where an explicit model of the environment is