Trained Model
A Trained Model is a software function produced by a model learning task/prediction task.
- AKA: Fitted Model.
- Context:
- It can be assessed with a Model Evaluation Task.
- It can be produced by a Model Training System (that solves a model training task).
- . It can be serialized by an ML Model Serialization System (such as Mleap).
- It can be managed by a ML Model Management System.
- It can be deployed by an ML Model Deployment System.
- It can be a component of a Trained Model Workflow (along with model data preparation code).
- …
- Example(s):
- a Trained Classification Model, such as the trained decision tree (trained on the iris dataset).
- a Trained Ranking Function.
- a Fitted Regression Model, such as the Regressed Linear Model of LLSE[math](\lt 1,6\gt , \lt 2,5\gt , \lt 3,7\gt , \lt 4,10\gt ) \Rightarrow y=1.4x+3.5[/math]
- a Trained Clustering Model, such as a trained k-means model.
- a Fitted Latent Factors Model.
- a Pre-Trained Model.
- …
- Counter-Example(s):
- an Untrained Model.
- a Machine Learning Model Family, such as a Decision Tree Metamodel.
- See: Learned Predictor, Learned Data Model, Ontology Learning Task, Metamodel Design Task, Modeling Language, Statistical Model, Deductive Model.
References
2011
- http://code.google.com/apis/predict/docs/glossary.html
- When you train the Prediction API against a data set, it creates a model based on that specific training data. All queries for the Prediction API are sent to a model trained against a specific training data.
