Скачать или смотреть Implementing Cross-Validation with Scikit-learn

Implementing cross-validation in Scikit-learn is both simple and effective, thanks to its comprehensive set of tools and functions designed for model evaluation. The 'cross_val_score' function is at the core of Scikit-learn's cross-validation capabilities. It allows you to evaluate a model using K-Fold Cross-Validation with just a few lines of code. You can specify the number of folds, the scoring metric, and even pass in custom cross-validation strategies such as Stratified K-Folds. Furthermore, Scikit-learn's cross-validation tools seamlessly integrate with its pipelines, allowing you to evaluate complex workflows that include data preprocessing and model training in a single step. The library also provides cross-validation generators that offer more control over the splitting process, including options for shuffling and stratification. Whether you're working with regression, classification, or clustering algorithms, Scikit-learn's cross-validation functions provide a robust framework for model evaluation, helping you to ensure that your models are both accurate and generalizable.