#### DO NOT CHANGE THE BELOW CODE ####
from sklearn.datasets import load_wine
from sklearn.utils import shuffle
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import numpy as np
percent_train = 0.8
num_reps = 10
features, labels = load_wine(return_X_y = True)
class_0 = 0
class_1 = 1
features = features[(labels == class_0) | (labels == class_1)]
labels = labels[(labels == class_0) | (labels == class_1)]
num_data, num_features = features.shape
split = int(np.ceil(num_data*percent_train))
features, labels = shuffle(features, labels, random_state=1)
train_features, train_labels = (features[:split], labels[:split])
test_features, test_labels = (features[split:], labels[split:])
num_train_data = train_features.shape[0]
num_test_data = test_features.shape[0]
#### DO NOT CHANGE THE ABOVE CODE ####
# Use this to store your error rates for each repetition
error_rates = []
for repetition in range(num_reps):
#### ADD YOUR CODE HERE ####
print(‘Average error rate:’, np.average(error_rates))
print(‘Error rate standard deviation:’, np.std(error_rates))
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