#### DO NOT CHANGE THE BELOW CODE ####
from sklearn.datasets import load_wine
from sklearn.model_selection import KFold
Copyright By PowCoder代写 加微信 powcoder
from sklearn.utils import shuffle
import numpy as np
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
features, labels = shuffle(features, labels, random_state=1)
#### DO NOT CHANGE THE ABOVE CODE ####
# This holds the average error rate on the test folds for each value of k
k_error_rates = []
for k in [23, 51, 101]:
k_fold = KFold(n_splits=5)
# This holds the error rates on each of the 5 test folds for a specific value of k
error_rates = []
for train_idx, test_idx in k_fold.split(features):
train_features = features[train_idx]
train_labels = labels[train_idx]
test_features = features[test_idx]
test_labels = labels[test_idx]
#### ADD YOUR CODE HERE ####
k_error_rates.append(np.average(error_rates))
print(‘Average test error for each value of k:’, k_error_rates)
程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com