.. _model_selection:

Model Selection Module
======================
:mod:`pinard.model_selection`


The ``model_selection`` module in Pinard is designed specifically for splitting and cross-validation techniques. It offers a range of functionalities to split NIRS (Near-Infrared Spectroscopy) data into training and testing sets, employing various strategies including Kennard Stone, SPXY, random sampling, stratified sampling, and k-means. This module is a valuable resource for researchers and practitioners in the field of NIRS analysis.


Pinard's ``model_selection`` module provides different strategies to divide the NIRS data into training and testing sets. These strategies include:

- Kennard Stone: This strategy selects representative samples from the dataset based on their Euclidean distances, ensuring an evenly distributed representation.

- SPXY: SPXY is a technique that splits the dataset based on spatial information, aiming to minimize spatial autocorrelation.

- Random Sampling: This strategy randomly selects samples from the dataset, ensuring a diverse representation.

- Stratified Sampling: Stratified sampling divides the dataset while maintaining the proportions of different classes or categories, ensuring balanced representation.

- K-means: K-means clustering is employed to split the dataset into distinct groups, ensuring samples within each group are similar.

Cross-Validation Methods
------------------------

Pinard's ``model_selection`` module also supports cross-validation methods to evaluate model performance effectively. Cross-validation is a robust technique that assesses model generalization by iteratively training and testing the model on different subsets of the data. This module provides reliable and accurate model assessments through cross-validation.

Conclusion
----------

Pinard's ``model_selection`` module is a comprehensive tool for splitting and evaluating NIRS data. With its diverse range of splitting strategies and support for cross-validation methods, researchers and practitioners can perform robust and reliable model assessments in their NIRS analysis.