Advanced Cross-Validation Methods

When we’re working with complex models or small datasets, basic cross-validation strategies like k-fold might not provide the control or precision we need. In this recipe, we’ll explore two advanced techniques—leave-one-out cross-validation (LOOCV) and nested cross-validation. LOOCV is useful when we want to make the most out of limited data, while nested cross-validation helps us tune hyperparameters without data leakage between model selection and evaluation.

Getting ready

To begin, we’ll load a small dataset and set up the libraries needed for LOOCV and nested cross-validation. We’ll use ridge regression for demonstration, which benefits from regularization tuning.

1. Load the libraries:

   import numpy as np
   from sklearn.datasets import load_diabetes
   from sklearn.model_selection import LeaveOneOut, GridSearchCV, cross_val_score
   from sklearn.linear_model import Ridge

2. Load the dataset:

   data = load_diabetes()
   X = data.data...