模型介绍
是从训练数据中选取一部分更加有效的支持向量
代码
from sklearn.svm import SVR
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
boston = load_boston()
print(boston.DESCR)
X = boston.data
y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
#分析回归目标值的差异
print("The max target value is ", np.max(boston.target))
print("The min target value is ", np.min(boston.target))
print("The average value is ", np.mean(boston.target))
#分别初始化对特征和目标值的标准化器
ss_X = StandardScaler()
ss_y = StandardScaler()
#标准化处理
X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = np.array(y_train).reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)
linear_svr = SVR(kernel='linear')
linear_svr.fit(X_train, y_train)
linear_svr_y_predict = linear_svr.predict(X_test)
poly_svr = SVR(kernel='poly')
poly_svr.fit(X_train, y_train)
poly_svr_y_predict = poly_svr.predict(X_test)
rbf_svr = SVR(kernel='rbf')
rbf_svr.fit(X_train, y_train)
rbf_svr_y_predict = rbf_svr.predict(X_test)
print('R-squared value of linear SVR is', linear_svr.score(X_test, y_test))
print('The mean squared error of linear SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_y_predict)))
print('The mean absoluate error of linear SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_y_predict)) )
print('R-squared value of Poly SVR is', poly_svr.score(X_test, y_test))
print('The mean squared error of Poly SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_y_predict)))
print('The mean absoluate error of Poly SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_y_predict)) )
print('R-squared value of RBF SVR is', rbf_svr.score(X_test, y_test))
print('The mean squared error of RBF SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_y_predict)))
print('The mean absoluate error of RBF SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_y_predict)) )