sklearn-多項式迴歸

import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import  PolynomialFeatures
from sklearn.linear_model import  LinearRegression

#載入數據
data = np.genfromtxt("job.csv",delimiter=",")

x_data = data[1:, 1, np.newaxis]
y_data = data[1:, 2, np.newaxis]

model = LinearRegression()
model.fit(x_data, y_data)

# 定義多項式迴歸，degree的值可以調節多項式的特徵
poly_reg = PolynomialFeatures(degree=3)
#特徵處理
x_poly = poly_reg.fit_transform(x_data)
# 定義迴歸模型
lin_reg = LinearRegression()
#訓練模型
lin_reg.fit(x_poly,y_data)

# 畫圖
plt.plot(x_data,y_data, 'b.' )
plt.plot(x_data, lin_reg.predict(poly_reg.fit_transform(x_data)), c='r')
plt.title("Truth of Bluff (Polynomian Regression)")
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()