Python机器学习（使用sklearn模块）之wine划分，聚类，分类，回归

我这里用的是sklearn自带的数据集中的wine，先提供一下所有需要用到的包吧（如果用的编译器是pycharm，以下所有代码需要放到一起执行）

from sklearn.datasets import load_wine#wine数据集
from sklearn.cluster import KMeans#K-Means聚类模型
from sklearn.model_selection import train_test_split#数据集划分
from sklearn.preprocessing import StandardScaler#标准差标准化
from sklearn.decomposition import PCA#pca降维
from sklearn.linear_model import LinearRegression#线性回归模型
from sklearn.metrics import fowlkes_mallows_score,silhouette_score,accuracy_score,\
    precision_score,recall_score,f1_score,cohen_kappa_score,classification_report,roc_curve,\
    explained_variance_score,mean_absolute_error,mean_squared_error,median_absolute_error,r2_score    #聚类、分类、回归评分标准
from sklearn.svm import SVC#SVM分类模型
import matplotlib.pyplot as plt#数据可视化
import numpy as np#·numpy科学计算包

1.sklearn转换器处理wine数据集

wine = load_wine()
data = wine['data']
target = wine['target']
#数据集划分为训练集，测试集
data_train,data_test,target_train,target_test = train_test_split(