AdaBoost

Machine Learning with Tree-Based Models in Python

Elie Kawerk

Data Scientist

Boosting

  • Boosting: Ensemble method combining several weak learners to form a strong learner.

  • Weak learner: Model doing slightly better than random guessing.

  • Example of weak learner: Decision stump (CART whose maximum depth is 1).

Machine Learning with Tree-Based Models in Python

Boosting

  • Train an ensemble of predictors sequentially.

  • Each predictor tries to correct its predecessor.

  • Most popular boosting methods:

    • AdaBoost,

    • Gradient Boosting.

Machine Learning with Tree-Based Models in Python

Adaboost

  • Stands for Adaptive Boosting.

  • Each predictor pays more attention to the instances wrongly predicted by its predecessor.

  • Achieved by changing the weights of training instances.

  • Each predictor is assigned a coefficient $\alpha$.

  • $\alpha$ depends on the predictor's training error.

Machine Learning with Tree-Based Models in Python

AdaBoost: Training

ada-train

Machine Learning with Tree-Based Models in Python

Learning Rate

Learning rate: $0 < \eta \leq 1$ ada-lr

Machine Learning with Tree-Based Models in Python

AdaBoost: Prediction

  • Classification:

    • Weighted majority voting.
    • In sklearn: AdaBoostClassifier.

  • Regression:

    • Weighted average.
    • In sklearn: AdaBoostRegressor.
Machine Learning with Tree-Based Models in Python

AdaBoost Classification in sklearn (Breast Cancer dataset)

# Import models and utility functions
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

# Set seed for reproducibility
SEED = 1

# 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,
                                                    stratify=y,
                                                    random_state=SEED)
Machine Learning with Tree-Based Models in Python
# Instantiate a classification-tree 'dt'
dt = DecisionTreeClassifier(max_depth=1, random_state=SEED)


# Instantiate an AdaBoost classifier 'adab_clf'
adb_clf = AdaBoostClassifier(base_estimator=dt, n_estimators=100)



# Fit 'adb_clf' to the training set
adb_clf.fit(X_train, y_train)

# Predict the test set probabilities of positive class
y_pred_proba = adb_clf.predict_proba(X_test)[:,1]



# Evaluate test-set roc_auc_score
adb_clf_roc_auc_score = roc_auc_score(y_test, y_pred_proba)
Machine Learning with Tree-Based Models in Python

AdaBoost Classification in sklearn (Breast Cancer dataset)

# Print adb_clf_roc_auc_score
print('ROC AUC score: {:.2f}'.format(adb_clf_roc_auc_score)) 

ROC AUC score: 0.99
Machine Learning with Tree-Based Models in Python

Let's practice!

Machine Learning with Tree-Based Models in Python

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