基于鸢尾花数据和手写数字，决策树，随机森林，voting，bagging法的比较

四种方法的具体的原理可以见博文
和西瓜书
先上代码根据结果分析

鸢尾花数据

# evaluate bagging algorithm for classification
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# 我们使用多个KNN模型作为基模型演示投票法，其中每个模型采用不同的邻居值K参数：
# get a voting ensemble of models
def base_modellist():
    # define the base models
    models = list()
    models.append(('DT1', DecisionTreeClassifier(max_depth=1)))
    models.append(('DT2',  DecisionTreeClassifier(max_depth=2)))
    models.append(('DT3',  DecisionTreeClassifier(max_depth=3)))
    # define the voting ensemble
    return models
def get_models():
    models = dict()
    models['DT1'] = DecisionTreeClassifier(max_depth=1)
    models['DT2'] = DecisionTreeClassifier(max_depth=2)
    models['DT3'] =  DecisionTreeClassifier(max_depth=3)
    models['DT100'] =  DecisionTreeClassifier(max_depth=100)
    models['bagging1'] = BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),n_estimators=100,max_samples=0.3)
    models['bagging3'] = BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=3),n_estimators=100