| 知乎专栏 |
n_samples：样本数
n_features：特征数(自变量个数)
n_informative：参与建模特征数
n_targets：因变量个数
noise：噪音
bias：偏差(截距)
coef：是否输出coef标识
random_state：随机状态若为固定值则每次产生的数据都一样，相当于随机种子
from sklearn import datasets import matplotlib.pyplot as plt x,y=datasets.make_regression(n_samples=100,n_features=2,n_targets=2,noise=2) plt.figure() plt.scatter(x,y) plt.show()
通过 NumPy 用一次多项式拟合，相当于线性拟合
from sklearn.datasets import make_regression import matplotlib.pyplot as plt import numpy as np x, y = make_regression(n_samples=10, n_features=1, n_targets=1, noise=1.5, random_state=1) plt.figure() plt.scatter(x, y); z1 = np.polyfit(x.reshape(10), y, 1) p1 = np.poly1d(z1) print(z1) print(p1) y1 = z1[0] * x + z1[1] plt.plot(x, y1, c='green') plt.show()