# Import functions to compute accuracy and split data
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
# Import models, including VotingClassifier meta-model
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier as KNN
from sklearn.ensemble import VotingClassifier
# Set seed for reproducibility
SEED = 1
sklearn 的 Voting Classifier（Breast-Cancer 資料集）
# Split data into 70% train and 30% test
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size= 0.3,
random_state= SEED)
# Instantiate individual classifiers
lr = LogisticRegression(random_state=SEED)
knn = KNN()
dt = DecisionTreeClassifier(random_state=SEED)
# Define a list called classifier that contains the tuples (classifier_name, classifier)
classifiers = [('Logistic Regression', lr),
('K Nearest Neighbours', knn),
('Classification Tree', dt)]
# Iterate over the defined list of tuples containing the classifiers
for clf_name, clf in classifiers:
#fit clf to the training set
clf.fit(X_train, y_train)
# Predict the labels of the test set
y_pred = clf.predict(X_test)
# Evaluate the accuracy of clf on the test set
print('{:s} : {:.3f}'.format(clf_name, accuracy_score(y_test, y_pred)))
Logistic Regression: 0.947
K Nearest Neighbours: 0.930
Classification Tree: 0.930
sklearn 的 Voting Classifier（Breast-Cancer 資料集）
# Instantiate a VotingClassifier 'vc'
vc = VotingClassifier(estimators=classifiers)
# Fit 'vc' to the traing set and predict test set labels
vc.fit(X_train, y_train)
y_pred = vc.predict(X_test)
# Evaluate the test-set accuracy of 'vc'
print('Voting Classifier: {.3f}'.format(accuracy_score(y_test, y_pred)))
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