机器学习算法之不同SVM惩罚参数C值不同效果⽐较
大学英语三级词汇表相亲的程序员太苦逼了
⾔归正传：来看看使⽤SVM惩罚参数C值效果对⽐svm1 = SVC(C=0.1,
kernel=‘rbf’)
svm2 = SVC(C=1,
kernel=‘rbf’)
svm3 = SVC(C=10,
kernel=‘rbf’)
svm4 = SVC(C=100,
kernel=‘rbf’)
svm5 = SVC(C=500,
雏妓什么意思
kernel=‘rbf’)
svm6 = SVC(C=100000,
kernel=‘rbf’)
以下是具体代码：
import time
import numpy as np
import pandas as pdpointat
犀利什么意思
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.svm import SVC
outbreak
del_lection import train_test_split ics import accuracy_score
## 设置属性防⽌中⽂乱码
# 'pal length', 'pal width', 'petal length', 'petal width'
timooiris_feature =u'花萼长度',u'花萼宽度',u'花瓣长度',u'花瓣宽度'
path ='./datas/iris.data'# 数据⽂件路径
data = pd.read_csv(path, header=None)
x, y = data[list(range(4))], data[4]
y = pd.Categorical(y).codes
x = x[[0,1]]
## 数据分割
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6) ## 数据SVM分类器构建
svm1 = SVC(C=0.1, kernel='rbf')
svm2 = SVC(C=1, kernel='rbf')
svm3 = SVC(C=10, kernel='rbf')
svm4 = SVC(C=100, kernel='rbf')
svm5 = SVC(C=500, kernel='rbf')
svm6 = SVC(C=100000, kernel='rbf')
#C越⼤，泛化能⼒越差，会出现过拟合的问题
#C越⼩，泛化能⼒越好，但是容易出现⽋拟合的问题
## 模型训练dinner是什么意思
t0=time.time()
svm1.fit(x_train, y_train)
t1=time.time()
svm2.fit(x_train, y_train)
t2=time.time()
svm3.fit(x_train, y_train)
t3=time.time()
svm4.fit(x_train, y_train)
t4=time.time()
svm5.fit(x_train, y_train)
t5=time.time()
svm6.fit(x_train, y_train)
t6=time.time()
svm1_score1 = accuracy_score(y_train, svm1.predict(x_train))
svm1_score2 = accuracy_score(y_test, svm1.predict(x_test))
svm2_score1 = accuracy_score(y_train, svm2.predict(x_train))
如何学好英语口语
lo过去式
svm2_score2 = accuracy_score(y_test, svm2.predict(x_test))
svm3_score1 = accuracy_score(y_train, svm3.predict(x_train))
svm3_score2 = accuracy_score(y_test, svm3.predict(x_test))
svm4_score1 = accuracy_score(y_train, svm4.predict(x_train))
svm4_score2 = accuracy_score(y_test, svm4.predict(x_test))
svm5_score1 = accuracy_score(y_train, svm5.predict(x_train))
svm5_score2 = accuracy_score(y_test, svm5.predict(x_test))
svm6_score1 = accuracy_score(y_train, svm6.predict(x_train))
svm6_score2 = accuracy_score(y_test, svm6.predict(x_test))
## 画图
x_tmp =[0,1,2,3,4,5]
t_score =[t1 - t0, t2-t1, t3-t2, t4-t3, t5-t4, t6-t5]
y_score1 =[svm1_score1, svm2_score1, svm3_score1, svm4_score1, svm5_score1, svm6_score1]
英汉互译在线转换y_score2 =[svm1_score2, svm2_score2, svm3_score2, svm4_score2, svm5_score2, svm6_score2]
plt.figure(facecolor='w', figsize=(12,6))
plt.subplot(121)
plt.plot(x_tmp, y_score1,'r-', lw=2, label=u'训练集准确率')
plt.plot(x_tmp, y_score2,'g-', lw=2, label=u'测试集准确率')
plt.xlim(-0.3,3.3)
plt.ylim(np.min((np.min(y_score1), np.min(y_score2)))*0.9, np.max((np.max(y_score1), np.max(y_score2)))*1.1) plt.legend(loc ='lower left')
plt.title(u'模型预测准确率', fontsize=13)
plt.subplot(122)
plt.plot(x_tmp, t_score,'b-', lw=2, label=u'模型训练时间')
plt.title(u'模型训练耗时', fontsize=13)
plt.suptitle(u'鸢尾花数据SVM分类器不同内核函数模型⽐较', fontsize=16)
plt.show()
