sklearn中常用的分类算法汇总及svm分类模型
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sklearn中常用的分类算法（模块名–函数名–算法名)：
（1) linear_model LogisticRegression 逻辑回归
>>> from sklearn.linear_model import LogisticRegression
>>> clf_l1_LR = LogisticRegression(C=C, penalty='l1', tol=0.01)
>>> clf_l2_LR = LogisticRegression(C=C, penalty='l2', tol=0.01)
（2）svm SVC 支持向量机
>>> from sklearn import svm
>>> clf = svm.SVC()
（3）neighbors KNeighborsClassifier knn近邻分类
>>> from sklearn.neighbors import NearestNeighbors
>>> nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)
（4）naive_bayes GaussianNB 朴素贝叶斯
>>> from sklearn.naive_bayes import GaussianNB
>>> gnb = GaussianNB()
（5）tree Decision TreeClassifier 分类决策树
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
（6）ensemble RandomForestClassifier 随机森林分类
>>> from sklearn.ensemble import RandomForestClassifier
>>> clf = RandomForestClassifier(n_estimators=10)
（7）Kmeans算法
>>> from sklearn.cluster import KMeans
>>> kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
（8）层次聚类（Hierarchical clustering）——支持多种距离
>>> from sklearn.cluster import AgglomerativeClustering
>>> model = AgglomerativeClustering(linkage=linkage,
connectivity=connectivity, n_clusters=n_clusters)
使用sklearn估计器构建SVM模型
##导入各模块和所需函数
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
##cancer数据集特征
cancer = load_breast_cancer()
cancer_data = cancer['data']
cancer_target = cancer['target']
cancer_names = cancer['feature_names']
## 将数据划分为训练集测试集
cancer_data_train,cancer_data_test, cancer_target_train,cancer_target_test = \
train_test_split(cancer_data,cancer_target,test_size = 0.2,random_state = 22)
## 数据标准化
stdScaler = StandardScaler().fit(cancer_data_train)
cancer_trainStd = stdScaler.transform(cancer_data_train)
cancer_testStd = stdScaler.transform(cancer_data_test)
## 建立SVM模型
svm = SVC().fit(cancer_trainStd,cancer_target_train)
print('建立的SVM模型为：\n',svm)
## 预测训练集结果
cancer_target_pred = svm.predict(cancer_testStd)
print('预测前20个结果为：\n',cancer_target_pred[:20])
将预测结果和真实结果做对比，求出准确率，代码如下：
## 求出预测和真实一样的数目
true = np.sum(cancer_target_pred == cancer_target_test )
print('预测对的结果数目为：', true)
print('预测错的的结果数目为：', cancer_target_test.shape[0]-true)
print('预测结果准确率为：', true/cancer_target_test.shape[0])
单单准确率并不能很好的反映模型的性能，为了有效的判断一个预测模型的效能表现，需要结合真实值计算出精确率，召回率，F1值，Cohen’s Kappa系数等指标。详情见下：
方法名称——最佳值——sklearn函数
Precision(精确率) 1.0 metrics.precision_score
Recall(召回率) 1.0 metrics.recall_score
F1值 1.0 metrics.f1_score
Cohen’s Kappa系数1.0 metrics.cohen_kappa_score
ROC曲线 最靠近y轴 metrics.roc_curve
代码如下：
from sklearn.metrics import accuracy_score,precision_score, \
recall_score,f1_score,cohen_kappa_score
print('使用SVM预测breast_cancer数据的准确率为：',
accuracy_score(cancer_target_test,cancer_target_pred))
print('使用SVM预测breast_cancer数据的精确率为：',
precision_score(cancer_target_test,cancer_target_pred))
print('使用SVM预测breast_cancer数据的召回率为：',
recall_score(cancer_target_test,cancer_target_pred))
print('使用SVM预测breast_cancer数据的F1值为：',
f1_score(cancer_target_test,cancer_target_pred))
print('使用SVM预测breast_cancer数据的Cohen’s Kappa系数为：',
cohen_kappa_score(cancer_target_test,cancer_target_pred))
另外，sklearn的metrics模块除了提供precision等单一评价指标的函数外，还提供了一个能输出分类模型评价报告的函数classification_report，代码如下：
from sklearn.metrics import classification_report
print('使用SVM预测iris数据的分类报告为：','\n',
classification_report(cancer_target_test,
cancer_target_pred))
除此之外，还可以用ROC曲线的方式来评价分类模型，代码如下：
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
## 求出ROC曲线的x轴和y轴
fpr, tpr, thresholds = roc_curve(cancer_target_test,cancer_target_pred)
plt.figure(figsize=(10,6))
plt.xlim(0,1) ##设定x轴的范围
plt.ylim(0.0,1.1) ## 设定y轴的范围
plt.xlabel('False Postive Rate')
plt.ylabel('True Postive Rate')
plt.plot(fpr,tpr,linewidth=2, linestyle="-",color='red')
plt.show()
转自：https://blog.csdn.net/qq_20412595/article/details/82192927
https://blog.csdn.net/zm_1900/article/details/89106643