支持向量回归

Question

执行此代码后，y_pred 太高了

我试过我的代码

import numpy as py
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:,1:2].values
y= dataset.iloc[:, 2].values

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))
# Fitting SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)
# Predicting a new result
y_pred=regressor.predict([[6.5]])
y_pred = sc_y.inverse_transform(y_pred)

为什么y_pred的价值这么高？我的代码有什么错误吗

Answer 1

我找到了解决方案：

我需要使用

而不是第 31 行和第 32 行

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

支持向量回归

Support vector regression

machine-learning

svm

python-3.x

scikit-learn