The AI Interview - Master AI/ML Interviews

Implement a function to generate train and test index splits for K-Fold Cross-Validation. Your task is to divide dataset indices into k folds and return a list of train-test index pairs for each fold.

Input:

n_samples: The total number of samples in the dataset (integer)
k: The number of folds (default 5)
shuffle: Whether to shuffle indices before splitting (default True)

Output:

A list of k tuples, where each tuple contains (train_indices, test_indices) as Python lists of integers

Requirements:

Split the indices into k roughly equal folds. If n_samples is not divisible by k, distribute the extra samples to the first folds (e.g., with 10 samples and k=3, fold sizes would be [4, 3, 3]).
For each fold iteration, use that fold as the test set and combine all other folds as the training set.
When shuffle=True, use np.random.shuffle() to randomize indices before splitting (the random seed will be set externally before calling your function).
When shuffle=False, keep indices in their original order [0, 1, 2, ..., n_samples-1].

Examples

Example 1:

Input: k_fold_cross_validation(n_samples=10, k=5, shuffle=False)

Output:

[([2, 3, 4, 5, 6, 7, 8, 9], [0, 1]), ([0, 1, 4, 5, 6, 7, 8, 9], [2, 3]), ([0, 1, 2, 3, 6, 7, 8, 9], [4, 5]), ([0, 1, 2, 3, 4, 5, 8, 9], [6, 7]), ([0, 1, 2, 3, 4, 5, 6, 7], [8, 9])]

Explanation: With 10 samples and k=5, we create 5 folds of 2 samples each. In each iteration, one fold becomes the test set (2 samples) and the remaining 4 folds become the training set (8 samples). Every sample appears in the test set exactly once across all iterations.

Starter Code

import numpy as np
from typing import List, Tuple

def k_fold_cross_validation(n_samples: int, k: int = 5, shuffle: bool = True) -> List[Tuple[List[int], List[int]]]:
    """
    Generate train/test index splits for k-fold cross-validation.
    
    Args:
        n_samples: Total number of samples in the dataset
        k: Number of folds (default 5)
        shuffle: Whether to shuffle indices before splitting (default True)
    
    Returns:
        List of (train_indices, test_indices) tuples
    """
    # Your code here
    pass

Implement K-Fold Cross-Validation

Examples

Starter Code