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This snippet demonstrates the implementation of a gradient descent algorithm for training a model, along with K-Fold cross-validation for evaluating its performance. It outlines the steps from data splitting to calculating test error, making it a great reference for machine learning practitioners.

# Step 1: Split the data
(X_train, X_test), (y_train, y_test) = split_data(X, y, test_size=0.2)

# Step 2: Train the model on the training data
theta, cost_history = gradient_descent(X_train, y_train)

# Step 3: Make predictions on the test data
predictions = predict(X_test, theta)

# Step 4: Calculate error between predictions and actual test labels
test_error = calculate_error(predictions, y_test)

# Step 5: Output the test error
print(test_error)

# K-Fold Cross-Validation Pseudo Code

# Step 1: Initialize K-Fold cross-validation
kfold = initialize_kfold(n_splits=5)

# Step 2: For each fold in the K-Fold cross-validation
for train_indices, test_indices in kfold.split(X):

    # Step 3: Split the data into training and test sets for this fold
    X_train, X_test = X[train_indices], X[test_indices]
    y_train, y_test = y[train_indices], y[test_indices]

    # Step 4: Train the model using the training data
    theta, cost_history = gradient_descent(X_train, y_train)

    # Step 5: Make predictions on the test data
    predictions = predict(X_test, theta)

    # Step 6: Calculate the error between predictions and actual test labels
    fold_error = calculate_error(predictions, y_test)

    # Step 7: Output or store the fold error
    print(fold_error)

# Step 8: Optionally, calculate the average error across all folds
average_error = calculate_average_error(all_fold_errors)
print(average_error)

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