# Number of rounds of bagging
L = 100
# Weights for selecting samples in each bootstrap
weights = np.ones((N,1),dtype=float)/N
# Storage of trained log.reg. classifiers fitted in each bootstrap
logits = [0]*L
votes = np.zeros((N,))
# For each round of bagging
for l in range(L):
# Extract training set by random sampling with replacement from X and y
X_train, y_train = bootstrap(X, y, N, weights)
# Fit logistic regression model to training data and save result
logit_classifier = LogisticRegression()
logit_classifier.fit(X_train, y_train)
logits[l] = logit_classifier
y_est = logit_classifier.predict(X).T
votes = votes + y_est
ErrorRate = (y!=y_est).sum(dtype=float)/N
print('Error rate: {:2.2f}%'.format(ErrorRate*100))
# Estimated value of class labels (using 0.5 as threshold) by majority voting
y_est_ensemble = votes>(L/2)
# Compute error rate
# Number of rounds of bagging
L = 100
# Weights for selecting samples in each bootstrap
weights = np.ones((N,1),dtype=float)/N
# Storage of trained log.reg. classifiers fitted in each bootstrap
logits = [0]*L
votes = np.zeros((N,1))
# For each round of bagging
for l in range(L):
# Extract training set by random sampling with replacement from X and y
X_train, y_train = bootstrap(X, y, N, weights)
# Fit logistic regression model to training data and save result
logit_classifier = LogisticRegression()
logit_classifier.fit(X_train, y_train.A.ravel())
logits[l] = logit_classifier
y_est = np.mat(logit_classifier.predict(X)).T
votes = votes + y_est
ErrorRate = (y!=y_est).sum(dtype=float)/N
print('Error rate: {0}%'.format(ErrorRate*100))
# Estimated value of class labels (using 0.5 as threshold) by majority voting
y_est_ensemble = votes>(L/2)
# Compute error rate
