CODE EXAMPLE FOR PYTHON

k fold cross validation python from scratch

#this function is used for KNN model. Where K represents the number of nearest neighbours.
def cross_validation(train_X, train_y, num_folds=4, k=1):
    dataset = list()
    dataset_split = list()
    val_acc = list()
    
    for i in range(len(train_X)):
        data = np.append(train_X[i],train_y[i])
        dataset.append(data)
    
    dataset_copy = list(dataset)
    fold_size = int(len(dataset) / num_folds)
    
    for i in range(num_folds):
        fold = list()
        while len(fold) < fold_size:
            index = randrange(len(dataset_copy))
            fold.append(dataset_copy.pop(index))
        dataset_split.append(fold)
        
    for folds in dataset_split:
        train_set= folds
        train_set = np.array(train_set)
        test_set = list()
        for row in folds:
            row_copy = list(row)
            test_set.append(row_copy)
            row_copy[-1] = None
        test_set = np.array(test_set)
        train_x = train_set[:, :-1]
        train_y = train_set[:,-1]
        test_x = test_set[:, :-1]
        predicted = predict(train_x,train_y, test_x, k)
        actual = [row[-1] for row in fold]
        accuracy = compute_accuracy(actual, predicted)
        val_acc.append(accuracy)
        
    val_acc_var = statistics.variance(val_acc)
    vall_acc = sum(val_acc)/len(val_acc)

    return vall_acc, val_acc_var
    
  #If this works, you can buy me a coffee.
# #https://www.buymeacoffee.com/eyolve