24/8/6算法笔记 不同核函数
import numpy as np
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt加载数据
X,y=datasets.load_wine(return_X_y = True)
display(X.shape)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
X_train.shape
建模
线性linear
svc = SVC(kernel = 'linear')svc.fit(X_train,y_train)y_pred = svc.predict(X_test)score = accuracy_score(y_test,y_pred)
print('使用核函数为linear，得分为：',score)
#二维：shape (3,13),
#三分类问题-->三个方程
#特征13个，所以系数是13
svc.coef_
svc.intercept_
poly多项式(方程幂次大于1）
svc = SVC(kernel = 'poly')#升维，数据由少变多svc.fit(X_train,y_train)y_pred = svc.predict(X_test)score = accuracy_score(y_test,y_pred)print('使用核函数为linear，得分为：',score)
rbf高斯分布,正态分布
svc = SVC(kernel = 'rbf')#默认的，一般这种核函数效果好，属于正态分布svc.fit(X_train,y_train)y_pred = svc.predict(X_test)score = accuracy_score(y_test,y_pred)print('使用核函数为linear，得分为：',score)
sigmoid函数
svc = SVC(kernel = 'sigmoid')svc.fit(X_train,y_train)y_pred = svc.predict(X_test)score = accuracy_score(y_test,y_pred)print('使用核函数为linear，得分为：',score)
非线性核函数
from matplotlib.colors import ListedColormap创造数据
X,y= datasets.make_circles(n_samples=100,factor=0.7)X +=np.random.randn(100,2)*0.03
display(X.shape,y.shape)plt.figure(figsize= (5,5))
cmap = ListedColormap(colors= ['blue','red'])
plt.scatter(X[:,0],X[:,1],c=y,cmap = cmap)
线性核函数
svc = SVC(kernel = 'linear')svc.fit(X,y)svc.score(X,y)
多项式poly(升维)
svc = SVC(kernel = 'poly',degree=2)#二次幂svc.fit(X,y)svc.score(X,y)
高斯核函数rbf
svc = SVC(kernel = 'rbf')svc.fit(X,y)svc.score(X,y)