机器学习分类模型的选择

分别用逻辑回归、线性回归、K近邻、决策树、贝叶斯和SVM6个算法对iris数据集进行分类，并采用交叉验证计算模型的准确率。

加载一些库：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from matplotlib import pyplot

加载数据集：

#加载数据集
data = load_iris()
X = data['data']
y = data['target']

训练集和测试集的划分：