Ensemble Learning

Machine Learning with Tree-Based Models in Python

Elie Kawerk

Data Scientist

Advantages of CARTs

Simple to understand.
Simple to interpret.
Easy to use.
Flexibility: ability to describe non-linear dependencies.
Preprocessing: no need to standardize or normalize features, ...

Limitations of CARTs

Classification: can only produce orthogonal decision boundaries.
Sensitive to small variations in the training set.
High variance: unconstrained CARTs may overfit the training set.
Solution: ensemble learning.

Ensemble Learning

Train different models on the same dataset.
Let each model make its predictions.
Meta-model: aggregates predictions of individual models.
Final prediction: more robust and less prone to errors.
Best results: models are skillful in different ways.

Ensemble Learning: A Visual Explanation

ensemble-visual

Ensemble Learning in Practice: Voting Classifier

Binary classification task.
$N$ classifiers make predictions: $P_1$, $P_2$, ..., $P_N$ with $P_i$ = 0 or 1.
Meta-model prediction: hard voting.

Hard Voting

hard-voting

Voting Classifier in sklearn (Breast-Cancer dataset)

# 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

Voting Classifier in sklearn (Breast-Cancer dataset)

# 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

Voting Classifier in sklearn (Breast-Cancer dataset)

# 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)))

Voting Classifier: 0.953

Let's practice!

Machine Learning with Tree-Based Models in Python