Implement bagging with decision trees

Q: Implement bagging with decision trees

This question evaluates a candidate's competency in ensemble learning and practical implementation skills, including bootstrap sampling, decision tree base learners, aggregation via majority voting with tie-breaking, and managing data using pure Python lists and standard library routines.

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Question

Implement a simple bagging (bootstrap aggregating) classifier that uses decision trees as base learners.

You are given a template with a DecisionTree class that supports at least:

fit(X, y)
predict(X)

Implement these methods for a BaggingClassifier (do not use NumPy; use Python lists and the standard library):

bootstrapping(X, y, seed=None) : sample n=len(X) training examples with replacement and return (X_sample, y_sample) .
fit(X, y) : for n_estimators trees, create a bootstrap sample and train one tree per sample.
predict(X) : aggregate predictions from all trees and output the final prediction per row (e.g., majority vote for classification). Specify how you break ties.

Assume X is a list of feature vectors and y is a list of class labels.

Implement bagging with decision trees

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