Implementing Cross-Validation in scikit-learn

Once we understand the theory and importance of cross-validation, the next step is to put it into practice. scikit-learn offers streamlined tools to implement different cross-validation workflows. In this recipe, we’ll walk through setting up basic and advanced cross-validation loops using cross_val_score(), cross_validate(), and GridSearchCV() to assess and compare model performance.

Getting ready

We’ll use a classification problem as our basis and load the necessary tools from scikit-learn to apply cross-validation.

1. Load the libraries:

   import numpy as np
   from sklearn.datasets import make_classification
   from sklearn.linear_model import LogisticRegression
   from sklearn.model_selection import cross_val_score, cross_validate, GridSearchCV

2. Load and prepare the dataset:

   X, y = make_classification(n_samples=500, n_features=10, weights=[0.7, 0.3], random_state=2024)

We will use 500 samples again. This time we will arbitrarily weight...