Implement bagging with decision trees

A DecisionTree class is available with fit(X, y) and predict(X). X is a list of feature vectors, and y is a list of class labels. Implement a BaggingClassifier that builds an ensemble of decision trees using bootstrap aggregating. Do not import NumPy; use Python lists and standard library modules only. The classifier must provide these methods: bootstrapping(X, y, seed=None) — select exactly len(X) indices with replacement, using the provided seed for reproducible randomness…