lr中的coef维度

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.linear_model import LogisticRegression # 构造一些数据点 centers = [[-5, 0], [0, 1.5], [5, -1]] X, y = make_blobs(n_samples=1000, centers=centers, random_state=40) print(X.shape) print(y.shape) print(y) transformation = [[0.4, 0.2], [-0.4, 1.2]] X = np.dot(X, transformation) print('______________') print(X.shape) clf = LogisticRegression().fit(X, y) print (clf.coef_) print (clf.intercept_) 

上述代码输出：

(1000, 2) (1000,) [0 0 0 2 1 1 0 0 0 1 2 2 2 1 1 1 1 2 0 2 0 1 0 1 1 2 0 1 1 1 2 0 2 0 2 1 2 2 0 0 1 0 0 0 1 2 0 0 0 2 0 0 0 1 1 0 1 2 0 1 2 1 2 2 0 1 1 0 1 0 1 2 1 1 0 2 1 2 0 0 1 1 2 0 0 2 1 2 1 2 2 2 1 1 2 2 0 0 0 0 2 2 0 1 0 0 1 2 2 1 2 1 0 1 1 2 0 2 1 1 0 1 2 2 2 0 0 1 0 2 0 2 2 1 2 1 0 1 2 2 2 2 2 1 1 1 1 2 2 2 1 2 1 0 0 1 1 1 1 1 2 0 0 1 2 2 2 0 1 2 1 1 1 0 1 2 0 2 2 0 2 2 1 1 2 0 1 0 0 2 1 1 0 1 0 1 1 0 0 0 1 2 2 1 0 0 1 0 1 1 2 0 0 0 1 2 1 2 1 2 0 2 1 1 0 2 2 1 1 1 0 0 2 2 2 0 2 2 2 2 1 0 2 2 1 0 2 2 0 0 2 0 2 0 2 0 2 0 2 2 0 1 1 1 1 2 1 1 1 2 1 2 0 1 2 0 0 2 2 2 0 2 0 0 1 1 1 2 1 0 2 0 0 0 2 2 0 2 2 1 2 0 0 2 1 1 1 0 1 2 2 2 0 0 2 0 0 1 1 0 0 0 0 0 2 2 1 2 2 1 2 1 1 2 0 0 1 0 0 2 2 1 1 0 1 0 0 2 2 2 1 1 2 0 0 2 2 2 1 1 0 1 2 1 1 2 1 2 0 0 0 2 1 0 1 1 1 1 1 1 2 0 1 0 1 2 2 0 2 1 2 1 2 2 0 1 2 1 0 0 1 2 2 0 2 1 0 2 2 1 0 1 1 2 1 2 2 1 2 0 1 2 0 2 1 2 0 1 2 2 1 0 0 2 1 2 1 0 0 1 1 2 2 1 1 0 0 2 0 2 0 0 0 0 1 0 0 0 2 0 2 2 2 0 0 0 1 2 0 0 1 2 0 1 2 1 0 2 0 0 2 0 2 2 1 1 1 0 1 2 1 1 0 0 1 1 1 2 0 2 0 1 2 1 0 2 2 2 0 2 1 1 2 1 2 0 1 0 2 2 1 0 0 1 0 1 2 2 2 2 2 2 0 2 2 0 2 2 2 2 0 2 2 0 0 0 2 2 0 1 1 1 0 1 0 1 0 2 0 2 2 1 0 0 2 0 0 2 1 0 0 0 1 1 2 0 0 2 2 0 2 2 0 2 2 1 1 1 1 0 0 1 0 1 0 2 1 1 0 0 2 1 2 0 1 2 2 1 0 1 1 0 0 2 0 0 2 1 1 0 0 0 2 0 1 1 2 1 0 0 1 2 1 1 1 1 0 2 0 2 2 2 0 1 0 1 0 0 1 1 0 2 2 0 0 0 0 2 1 2 1 0 1 2 2 0 0 1 2 2 1 2 0 2 2 0 2 1 1 2 0 0 0 2 0 1 2 1 2 2 2 1 1 1 1 1 2 2 0 1 0 2 1 1 0 0 2 1 0 0 2 0 0 2 1 2 2 2 2 1 0 0 1 0 2 0 0 0 0 1 0 1 0 0 0 1 2 2 2 0 2 0 0 2 2 0 1 2 2 2 0 1 0 2 1 1 2 1 2 0 0 1 1 0 1 1 0 0 1 0 2 0 0 0 1 2 0 0 2 0 0 1 0 2 0 2 2 1 1 2 0 0 1 2 2 2 1 2 2 1 0 2 0 0 2 0 1 1 1 0 2 2 1 0 0 2 2 1 0 1 1 1 1 2 1 2 1 0 1 0 1 2 0 0 1 0 1 1 2 0 1 0 1 2 2 1 0 2 0 1 0 0 0 0 2 0 1 2 1 2 2 1 1 1 2 1 0 1 1 1 0 2 2 0 0 1 2 0 1 1 1 0 0 2 0 1 0 1 1 2 2 0 2 2 0 0 0 1 1 0 0 2 0 2 2 1 2 2 0 0 1 1 2 1 1 2 1 0 1 1 0 1 1 0 0 2 0 1 1 1 1 2 2 1 2 0 1 2 1 1 0 2 0 0 0 1 2 0 1 2 1 2 1 1 1 0 0 1 1 2 2 1 1 1 1 0 2 2 2 1 1 2 2 1 1 0 0 0 2 2 1 1 0 0 1 0 2 1 1 2 2 0 2 2 1 2 1 0 1 2] ______________ (1000, 2) [[-4. -1.] [-0.0 0.] [ 4. 0.]] [-1. 2. -1.] 

大胆假设：

最后的coef是3×2的，

单看每一行，有2个数，代表2个特征的系数

一共3行，是因为总共3个类别。对于每一个类别，都学习一次系数

因此是3×2的。

refer：https://www.cnblogs.com/LCharles/p/12162077.html