回归问题是指找到一根直线/曲线最大程度的拟合样本点。
不同的回归算法对“拟合”有不同的理解。
例如线性回归算法定义“拟合”为：所有样本点到直线的MSE最小
而SVM将“拟合”定义为：尽可能多的点被包含在margin范围内，取margin中间的直线。（与解决分类问题相反的思路）
margin的距离为超参数
import numpy as np
from sklearn import datasets
boston = datasets.load_boston()
x = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=666)
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.svm import LinearSVR
from sklearn.pipeline import Pipeline
# SVR = Support Vector Regression
# SVR和SVC的用法基本上一样
def StandardLinearSVR(epsilon=0.1):
return Pipeline([
('std_scaler', StandardScaler()),
('linearSVR', LinearSVR(epsilon=epsilon))
])
svr = StandardLinearSVR()
svr.fit(X_train, y_train)
svr.score(X_test, y_test)
