File-based Prediction Structure: Difference between revisions
Jump to navigation
Jump to search
m (Text replacement - "([^\s])([\s]{1,7})\<P\>" to "$1 <P>") |
m (Text replacement - "\<P\>([\s]{1,7})([^\s])" to "<P> $2") |
||
| Line 19: | Line 19: | ||
=== 2014 === | === 2014 === | ||
* http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence | * http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence | ||
** After [[training]] a [[scikit-learn model]], it is desirable to have a way to [[persist]] [[the model]] for future use without having to retrain. The following section gives you an example of how to [[persist]] a model with pickle. We’ll also review a few security and maintainability issues when working with pickle serialization. … <P> … It is possible to save a model in the scikit by using Python’s built-in persistence model, namely pickle: | ** After [[training]] a [[scikit-learn model]], it is desirable to have a way to [[persist]] [[the model]] for future use without having to retrain. The following section gives you an example of how to [[persist]] a model with pickle. We’ll also review a few security and maintainability issues when working with pickle serialization. … <P> … It is possible to save a model in the scikit by using Python’s built-in persistence model, namely pickle: | ||
---- | ---- | ||
Latest revision as of 21:10, 18 August 2021
A File-based Prediction Structure is a prediction structure that is a file-based data structure.
- Context:
- It can assume a Feature Data Preparation Task.
- …
- Example(s):
- Counter-Example(s):
- See: PMML, PMML File.
References
2014
- http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence
- After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following section gives you an example of how to persist a model with pickle. We’ll also review a few security and maintainability issues when working with pickle serialization. …
… It is possible to save a model in the scikit by using Python’s built-in persistence model, namely pickle:
- After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following section gives you an example of how to persist a model with pickle. We’ll also review a few security and maintainability issues when working with pickle serialization. …