Voting

Ensemble Methods in Python

Román de las Heras

Data Scientist, Appodeal

Ask the audience

Wisdom of the crowd

Collective intelligence
Large group of individuals >= Single expert
Problem solving
Decision making
Innovation
Prediction

Majority voting

Properties

Classification problems
Majority Voting: Mode
Odd number of classifiers (3+)

Wise Crowd Characteristics:

Diverse: different algorithms or datasets
Independent and uncorrelated
Use individual knowledge
Aggregate individual predictions

Voting ensemble using scikit-learn

from sklearn.ensemble import VotingClassifier

clf_voting = VotingClassifier(
    estimators=[
       ('label1', clf_1), 
       ('label2', clf_2),
       ('labelN', clf_N)])

Evaluate the performance

# Get the accuracy score 
acc = accuracy_score(y_test, y_pred)
print("Accuracy: {:0.3f}".format(acc))

Accuracy: 0.938

# Create the individual models
clf_knn = KNeighborsClassifier(5)
clf_dt = DecisionTreeClassifier()
clf_lr = LogisticRegression()

# Create voting classifier
clf_voting = VotingClassifier(
    estimators=[
       ('knn', clf_knn), 
       ('dt', clf_dt), 
       ('lr', clf_lr)])

# Fit it to the training set and predict
clf_voting.fit(X_train, y_train)
y_pred = clf_voting.predict(X_test)

Let's give it a try!

Ensemble Methods in Python