Sklearn linear regression completes the fitting of multiple term function and sine function

These two formulas , Use sklearn Linear regression is used for fitting

Go straight to the code

import random
import numpy as np
from sklearn.linear_model import LinearRegression,Ridge
from sklearn.metrics import accuracy_score, mean_squared_error, r2_score,mean_squared_log_error
from sklearn.utils.multiclass import type_of_target
from sklearn.preprocessing import PolynomialFeatures # Polynomial feature processing 
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
plt.figure()

#data prepare
X = np.linspace(-50,50,100)
# X = np.sin(X)
print(X.shape)
y = 0.015*np.power(X,3)+0.07*np.power(X,2)+6*X+3+np.random.normal(0,100,100)


X = X.reshape(-1,1)
y = y.reshape(-1,1)

# Generate N Order polynomial 
degree = 3
ploy_features = PolynomialFeatures(degree=degree, include_bias=False)
model = LinearRegression(normalize=True) #  Linear regression model instantiation , Disease data standardization 
pipline = Pipeline([('ploy_features', ploy_features),('model',model)])
pipline.fit(X,y)
y_predict = pipline.predict(X)
train_score = pipline.score(X,y)
mse = mean_squared_error(y,y_predict)
print(train_score)
print(mse)
print(pipline.get_params())

plt.scatter(X,y, marker='o', c='r')
plt.scatter(X,y_predict,marker='o',c='g')
plt.show()

Get the results ：

score :   0.9902512046606555
mse :  7940.310765934783

Drawing results ：

Draw a picture of the original data of the Zhengxuan curve

degree Make a third-order fitting diagram

 degree Make a second-order fitting diagram

degree Determine the sixth order fitting diagram , The effect is very good , But I don't know if it's a little too fitting 、?

  Don't talk much , Go straight to the code ：

import random
import numpy as np
from sklearn.linear_model import LinearRegression,Ridge
from sklearn.metrics import accuracy_score, mean_squared_error, r2_score,mean_squared_log_error
from sklearn.utils.multiclass import type_of_target
from sklearn.preprocessing import PolynomialFeatures # Polynomial feature processing 
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
plt.figure()

#data prepare
X = np.linspace(-5,5,150)
# X = np.sin(X)
print(X.shape)
y = 0.5*np.sin(X)+np.random.normal(0,0.05,150)


X = X.reshape(-1,1)
y = y.reshape(-1,1)

# Generate N Order polynomial 
degree = 6
ploy_features = PolynomialFeatures(degree=degree, include_bias=False)
model = LinearRegression(normalize=True) #  Linear regression model instantiation , Disease data standardization 
pipline = Pipeline([('ploy_features', ploy_features),('model',model)])
pipline.fit(X,y)
y_predict = pipline.predict(X)
train_score = pipline.score(X,y)
mse = mean_squared_error(y,y_predict)
print(train_score)
print(mse)
print(pipline.get_params())

plt.scatter(X,y, marker='o', c='r')
plt.scatter(X,y_predict,marker='o',c='g')
plt.show()