Skip to content

MTN Added model state to the glossary #857

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Aug 13, 2025
Merged

MTN Added model state to the glossary #857

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3f65284
added model state to the glossary
Aug 13, 2025
3c41531
Update jupyter-book/appendix/glossary.md
SebastienMelo Aug 13, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 10 additions & 2 deletions jupyter-book/appendix/glossary.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -210,6 +210,14 @@ In scikit-learn jargon: an [estimator](#estimator) that takes another
Generic term that refers to something that can [learn](#train-learn-fit)
[prediction](#predict-prediction) rules from the data.

### model state

Set of numerical values that an [estimator](#estimator) learns during training.
They summarize patterns in the data (limited to what the [estimator](#estimator)
can represent), and are stored for later predictions or transformations.
Examples include the slope and intercept in a linear regression; or the
per-feature mean and standard deviation in a standard scaler.

### overfitting

Overfitting occurs when your [model](#model) stick too closely to the [training
Expand Down Expand Up @@ -357,8 +365,8 @@ In a more abstract manner, we can represent fitting with the following diagram:

![img](https://inria.github.io/scikit-learn-mooc/_images/api_diagram-predictor.fit.svg)

The model state are indeed the parameters and the jockey wheels are referring to
an optimization algorithm to find the best parameters.
The [model state](#model-state) are indeed the parameters and the jockey wheels
are referring to an optimization algorithm to find the best parameters.

### train set

Expand Down