Скачать или смотреть Understanding n_splits in sklearn.model_selection.GroupShuffleSplit

Discover what the `n_splits` parameter means in sklearn's GroupShuffleSplit and how it influences your data splitting strategy for machine learning models.
---
This video is based on the question https://stackoverflow.com/q/68983644/ asked by the user 'julliet' ( https://stackoverflow.com/u/9795226/ ) and on the answer https://stackoverflow.com/a/68983770/ provided by the user 'Antoine Dubuis' ( https://stackoverflow.com/u/4574633/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Please, explain what n_splits actually mean in sklearn.model_selection.GroupShuffleSplit?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding n_splits in sklearn.model_selection.GroupShuffleSplit

When working with machine learning, properly splitting your dataset into training and testing subsets is crucial for building robust models. This process becomes even more vital when you want to preserve the integrity of grouped data, where certain observations belong together—like different measurements from the same subject. A handy function from the scikit-learn library provides a solution: GroupShuffleSplit. However, there can be some confusion regarding its parameters, especially n_splits. In this article, we'll demystify what n_splits actually means and how it impacts your data splitting strategy.

The Challenge: Data Splitting with Groups

Suppose you have a dataset where the observations are organized into groups (such as participants in a study). When training a machine learning model, you want to ensure that the data in your training set and test set are representative but also respect the group structure. This is where GroupShuffleSplit shines. It allows you to shuffle your data and split it into training and testing sets while controlling those groups.

What Is n_splits?

The parameter n_splits in GroupShuffleSplit specifies the number of iterations the function will execute to generate different train/test splits. Simply put, it tells the function how many times to reshuffle and split the data based on the specified proportions.

Key Points about n_splits:

Iterations: It signifies how many different train-test splits you want to create. Each split is independent of the others.

Train/Test Size: It operates alongside train_size (and optionally test_size), which defines the proportion of the dataset used for training (in your case, 70% for training and 30% for testing).

Flexibility in Testing: Multiple splits allow you to compare different train/test sets. This is particularly useful for validating the robustness of your model.

How Does It Impact Your Data?

In your provided code snippet:

[[See Video to Reveal this Text or Code Snippet]]

you've set n_splits to 10. This means the following:

GroupShuffleSplit will create and return ten unique train/test split iterations.

Each iteration will ensure that the training set takes 70% of the total data while respecting the grouping structure.

However, when you call next(gs.split(...)), you're only retrieving one of those 10 possible splits. This is why you might see no significant change in your dataset size when varying n_splits—you are likely not using the additional splits in the iterations.

Example of Benefits of Using Multiple n_splits:

Model Evaluation: If you were to loop through all 10 splits for model training and evaluation, you could assess how stable your model's performance is across different data samples.

Reduced Variance: By training on various splits, you may better estimate the generalization error of your model.

Conclusion

In summary, the n_splits parameter in sklearn.model_selection.GroupShuffleSplit indicates how many reshuffles and splits you want to create for your training and testing datasets. While it does not directly influence the size of the datasets (which you define with train_size), it opens up the opportunity to analyze various subsets of your data, ultimately leading to a more robust evaluation of your machine learning model.

By making use of GroupShuffleSplit effectively, you can ensure that your models are well-validated, providing more accurate predictions when applied to unseen data. Happy coding!