代码：
from sklearn.datasets import load_iris, fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier,export_graphviz
def knn_iris():
# 用KNN 算法对鸢尾花进行分类
# 1、获取数据
iris = load_iris()
# 2、划分数据集
x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=6)
# 3、特征工程 - 标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4、KNN 算法预估器
estimator = KNeighborsClassifier(n_neighbors=3)
estimator.fit(x_train,y_train)
# 5、模型评估
# 方法1 ：直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict：\n",y_predict)
print("直接比对真实值和预测值：\n",y_test == y_predict)
# 方法2：计算准确率
score = estimator.score(x_test,y_test)
print("准确率为：\n",score)
return None
def knn_iris_gscv():
# 用KNN 算法对鸢尾花进行分类，添加网格搜索和交叉验证
# 1、获取数据
iris = load_iris()
# 2、划分数据集
x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=6)
# 3、特征工程 - 标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4、KNN 算法预估器
estimator = KNeighborsClassifier()
# 加入网格搜索和交叉验证
# 参数准备
param_dict = {"n_neighbors":[1,3,5,7,9,11]}
estimator = GridSearchCV(estimator,param_grid=param_dict,cv=10)
estimator.fit(x_train,y_train)
# 5、模型评估
# 方法1 ：直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict：\n",y_predict)
print("直接比对真实值和预测值：\n",y_test == y_predict)
# 方法2：计算准确率
score = estimator.score(x_test,y_test)
print("准确率为：\n",score)
# 最佳参数：best_params_
print("最佳参数：\n",estimator.best_params_)
# 最佳结果：best_score_
print("最佳结果：\n",estimator.best_score_)
# 最佳估计值：best_estimator_
print("最佳估计值：\n",estimator.best_estimator_)
# 交叉验证结果：cv_results_
print("交叉验证结果：\n",estimator.cv_results_)
return None
def nb_news():
# 用朴素贝叶斯算法对新闻进行分类
# 1、获取数据
news = fetch_20newsgroups(subset="all")
# 2、划分数据集
x_train,x_test,y_train,y_test = train_test_split(news.data,news.target)
# 3、特征工程：文本特征抽取-tfidf
transfer = TfidfVectorizer()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4、用朴素贝叶斯算法预估器流程
estimator = MultinomialNB()
estimator.fit(x_train,y_train)
# 5、模型评估
# 方法1 ：直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict：\n", y_predict)
print("直接比对真实值和预测值：\n", y_test == y_predict)
# 方法2：计算准确率
score = estimator.score(x_test, y_test)
print("准确率为：\n", score)
return None
def decision_iris():
# 用决策树对鸢尾花进行分类
# 1、获取数据集
iris = load_iris()
# 2、划分数据集
x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=22)
# 3、决策树预估器
estimator = DecisionTreeClassifier(criterion="entropy")
estimator.fit(x_train,y_train)
# 4、模型评估
# 方法1 ：直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict：\n", y_predict)
print("直接比对真实值和预测值：\n", y_test == y_predict)
# 方法2：计算准确率
score = estimator.score(x_test, y_test)
print("准确率为：\n", score)
# 可视化决策树
export_graphviz(estimator,out_file="iris_tree.dot",feature_names=iris.feature_names)
return None
if __name__ == "__main__":
# 代码1 ：用KNN算法对鸢尾花进行分类
# knn_iris()
# 代码2 ：用KNN算法对鸢尾花进行分类，添加网格搜索和交叉验证
# knn_iris_gscv()
# 代码3：用朴素贝叶斯算法对新闻进行分类
# nb_news()
# 代码4：用决策树对鸢尾花进行分类
decision_iris()
