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## Cross-Validation Data Split Implementation (medium)

#### Example

## Understanding k-Fold Cross-Validation Data Splitting

k-Fold cross-validation is a technique used to evaluate the generalizability of a model by dividing the data into `k` folds or subsets. Each fold acts as a test set once, with the remaining `k-1` folds serving as the training set. This approach ensures that every data point gets used for both training and testing, improving model validation.
### Steps in k-Fold Cross-Validation Data Split:

### Benefits of this Approach:

- Ensures all data is used for both training and testing.
- Reduces bias since each data point gets to be in a test set exactly once.
- Provides a more robust estimate of model performance.
Implementing this data split function will allow a deeper understanding of how data partitioning affects machine learning models and will provide a foundation for more complex validation techniques.

Write a Python function that performs k-fold cross-validation data splitting from scratch. The function should take a dataset (as a 2D NumPy array where each row represents a data sample and each column represents a feature) and an integer k representing the number of folds. The function should split the dataset into k parts, systematically use one part as the test set and the remaining as the training set, and return a list where each element is a tuple containing the training set and test set for each fold.

Example: input: data = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]), k = 5 output: [[[[3, 4], [5, 6], [7, 8], [9, 10]], [[1, 2]]], [[[1, 2], [5, 6], [7, 8], [9, 10]], [[3, 4]]], [[[1, 2], [3, 4], [7, 8], [9, 10]], [[5, 6]]], [[[1, 2], [3, 4], [5, 6], [9, 10]], [[7, 8]]], [[[1, 2], [3, 4], [5, 6], [7, 8]], [[9, 10]]]] reasoning: The dataset is divided into 5 parts, each being used once as a test set while the remaining parts serve as the training set.

**Shuffle the dataset randomly**. (but not in this case because we test for a unique result)**Split the dataset into k groups**.**Generate Data Splits**: For each group, treat that group as the test set and the remaining groups as the training set.

import numpy as np def cross_validation_split(data, k, seed): np.random.seed(seed) np.random.shuffle(data) # This line can be removed if shuffling is not desired in examples fold_size = len(data) // k folds = [] for i in range(k): start, end = i * fold_size, (i + 1) * fold_size if i != k-1 else len(data) test = data[start:end] train = np.concatenate([data[:start], data[end:]]) folds.append([train.tolist(), test.tolist()]) return folds

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