python 写SVR

下面是使用Python实现SVR的基本步骤：

1.导入必要的库：numpy、pandas和sklearn

import numpy as np
import pandas as pd
from sklearn.svm import SVR

2.读取数据集，将特征和目标分别存储到X和y中

data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

3.标准化数据集

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y.reshape(-1, 1))

4.拟合SVR模型

regressor = SVR(kernel='rbf')
regressor.fit(X, y)

5.进行预测

y_pred = regressor.predict(sc_X.transform([[6.5]]))
y_pred = sc_y.inverse_transform(y_pred)

完整代码如下：

import numpy as np
import pandas as pd
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler

# 读取数据集
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 标准化数据集
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y.reshape(-1, 1))

# 拟合SVR模型
regressor = SVR(kernel='rbf')
regressor.fit(X, y)

# 进行预测
y_pred = regressor.predict(sc_X.transform([[6.5]]))
y_pred = sc_y.inverse_transform(y_pred)
print(y_pred)