sklearn 线性回归

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline

x = np.linspace(0, 30, 20)

y = x + 3*np.random.randn(20)

plt.figure(figsize=(10, 8))
plt.scatter(x, y)

<matplotlib.collections.PathCollection at 0x1115cc208>

from sklearn.linear_model import LinearRegression

model = LinearRegression()

X = x.reshape(-1, 1)

Y = y.reshape(-1, 1)

model.fit(X, Y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

model.predict([[40]])

array([[38.18890435]])

plt.figure(figsize=(12, 8))
plt.scatter(X, Y)
plt.plot(X, model.predict(X))

[<matplotlib.lines.Line2D at 0x1a1c284e48>]

Y_PRE = model.predict(X)

np.sum(np.square(Y_PRE, Y))

5760.457790652543

model.coef_, model.intercept_

(array([[0.94170908]]), array([0.52054117]))