[人工智能]算法进阶--SVM实践

分类器指标（再谈）

在前面precision，recall 以及F1评判指标下引入$F_{\beta }$:
$$F_{\beta }=\frac{(1+\beta )?precision?recall}{{\beta }^2?precision+recall}$$

• 其中，${\beta }^2$越小，表明越重视precision

svm初步使用

import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.model_selection import  train_test_split
from sklearn.metrics import  accuracy_score


# 利用SVM对鸢尾花进行分类


#导入鸢尾花数据集
data = pd.read_csv('./iris.csv')
print(data)
#选择特征值X，为花萼长和花萼宽为和目标值Y
X,Y = data.iloc[:,[2,3]],data.iloc[:,5]
#将目标值Y分类成0,1,2 三个类别
Y=pd.Categorical(Y).codes
#拆分数据
x_train,x_test,y_train,y_test = train_test_split(X,Y,random_state=1,test_size=0.75)


#选择svm分类器并开始分类
clf = svm.SVC(C=0.1,kernel='linear',decision_function_shape='ovr')
clf.fit(x_train,y_train)


#准确率
y_hat = clf.predict(x_test)
print(y_hat)

print('准确率为：',clf.score(x_test,y_test))
print('准确率为：',accuracy_score(y_hat,y_test))

输出：

     Unnamed: 0  Sepal.Length  ...  Petal.Width    Species
0             1           5.1  ...          0.2     setosa
1             2           4.9  ...          0.2     setosa
2             3           4.7  ...          0.2     setosa
3             4           4.6  ...          0.2     setosa
4             5           5.0  ...          0.2     setosa
..          ...           ...  ...          ...        ...
145         146           6.7  ...          2.3  virginica
146         147           6.3  ...          1.9  virginica
147         148           6.5  ...          2.0  virginica
148         149           6.2  ...          2.3  virginica
149         150           5.9  ...          1.8  virginica

[150 rows x 6 columns]
[0 1 1 0 2 1 1 0 0 2 1 0 2 1 1 0 1 1 0 0 1 1 1 0 2 1 0 0 1 1 1 2 1 2 1 0 1
 0 1 2 2 0 1 2 1 2 0 0 0 1 0 0 2 2 2 2 1 1 2 1 0 1 1 0 0 2 0 1 1 1 1 2 1 0
 1 1 2 1 2 1 0 0 0 2 0 1 2 1 0 0 1 0 2 1 2 2 1 2 2 1 0 1 0 1 1 0 1 0 0 2 1
 2 0]
准确率为： 0.8938053097345132
准确率为： 0.8938053097345132

Process finished with exit code 0