简单的线性回归

import numpy as np
from sklearn.linear_model import LinearRegression

np.random.seed(1)

X = 2 * np.random.rand(10000, 1)
#模拟出10000个y
y = 4 + 3 * X + np.random.randn(10000, 1)
#实例化出一个线性回归器，是个对象
lin_reg = LinearRegression()
lin_reg.fit(X, y)
print(lin_reg.intercept_, lin_reg.coef_)

# X_new = np.array([[0], [2]])
X_new = np.array([[2]])
print(lin_reg.predict(X_new))