[英]Scikitlearn Linear Regression with 2 features
我是 scikit 学习的新手,正在尝试拟合一个简单的线性回归 model。我有一个包含 2 列 c1 和 c1^2 的矩阵 X,并且我有相应的 y 值。 我尝试使用 scikit learn 来拟合一个简单的 OLS model,但最后我变得很奇怪 plot。 关于我做错了什么的任何想法?
X = np.array([[ -0.016746535778021, 0.280446460564527],
[-0.014577470749242, 0.212502653445002],
[0.034515758657299, 1.191337595688933],
[-0.047010075743201, 2.209947221381472],
[0.036975119046363, 1.367159428492700],
[-0.040686110015367, 1.655359548182586],
[-0.004472010975766, 0.019998882167376],
[0.026533634894789 , 0.704033780729957],
[-0.042797683100180, 1.831641678743394],
[0.025374099383528, 0.643844919525139],
[-0.031109553977308, 0.967804348667025],
[0.027311768635213, 0.745932705983427],
[-0.003263862013657, 0.010652795244191],
[-0.001818276487116, 0.003306129383598],
[-0.040719662402516, 1.658090906174888],
[-0.050013243645495, 2.501324539943689],
[-0.017411771548016, 0.303169788440313],
[0.003588193696644, 0.012875134004637],
[0.007085480261971, 0.050204030542776,],
[0.046282369018539, 2.142057681968212],
[0.014612289091657, 0.213518992498145]])*1e3
y = np.array([4.1702,
4.0673,
31.8731,
10.6237,
31.8360,
4.9594,
4.4516,
22.2763,
-0.0000,
20.5038,
3.8583,
19.3651,
4.8838,
11.0972,
7.4617,
1.4769,
2.7192,
10.9269,
8.3487,
52.7819,
13.3573])
from sklearn.linear_model import LinearRegression as LR
model1 = LR().fit(X,y)
import matplotlib.pyplot as plt
plt.plot(X[:,0],model1.predict(X))
plt.scatter(X[:,0],y,color = 'red')
plt.show()
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