Bagging算法（Bootstrap aggranting）

1、Bagging trains n base learner in parallel

2、Make decisions by averaging learers' outputs(regeression) or majority voting(classfication)

3、Each learner is trained bootstrap sampling

　　·Assume m training examples,than randomly sampling m examples with replacement

　　·Around 1-1/e≈63% examples will be sampled,the rest(out-of-bag) can be used for validation

class Bagging:
    def __init__(self, base_learner, n_learners):
        self.learners = [clone(base_learner) for _ in range(n_learners)]

    def fit(self, X,Y):
        for learner in self.learners:
            examples = np.random.choice(np.arange(len(X)), int(len(X)), replacement=True)
            learners.fit(X.iloc[examples,:],Y.iloc[examples])

    def predict(self, X):
        preds = [learner.predict(X) for learner in self.learners]
        return np.array(preds).mean(axis=0)