Glossary term
Glossary term
Foundations
In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions. In supervised machine learning, a model takes an example as input and infers a prediction as output. Within supervised machine learning, models differ somewhat. For example:
A linear regression model consists of a set of weights and a bias.
A neural network model consists of:
A set of hidden layers, each containing one or more neurons.
The weights and bias associated with each neuron.
A decision tree model consists of:
The shape of the tree; that is, the pattern in which the conditions and leaves are connected.
The conditions and leaves.
You can save, restore, or make copies of a model.
Unsupervised machine learning also generates models, typically a function that can map an input example to the most appropriate cluster.
Click the icon to compare algebraic and programming functions to ML models.
Created for this library
A retail forecasting team manages a portfolio of models across SKU groups with shared monitoring and retraining schedules.
A risk team versions its production model in a registry so any audit can map outcomes back to the exact model that produced them.
A SaaS team owns model lifecycle from training through retirement with clear metrics for each phase.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License