scipy linregress的多变量线性回归 [英] Multi-variable linear regression with scipy linregress

问题描述

我正在尝试训练一个非常简单的线性回归模型.

I'm trying to train a very simple linear regression model.

我的代码是:

from scipy import stats

xs = [[   0,    1,  153]
 [   1,    2,    0]
 [   2,    3,  125]
 [   3,    1,   93]
 [   2,   24, 5851]
 [   3,    1,  524]
 [   4,    1,    0]
 [   2,    3,    0]
 [   2,    1,    0]
 [   5,    1,    0]]

ys = [1, 1, 1, 1, 1, 0, 1, 1, 0, 1]

slope, intercept, r_value, p_value, std_err = stats.linregress(xs, ys)

我遇到以下错误:

File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/stats/stats.py", line 3100, in linregress
ssxm, ssxym, ssyxm, ssym = np.cov(x, y, bias=1).flat
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/lib/function_base.py", line 1747, in cov
X = concatenate((X, y), axis)
ValueError: all the input array dimensions except for the concatenation 
axis must match exactly

我的输入有什么问题?我试图以几种方式更改 ys 的结构，但没有任何效果.

What's wrong with my input? I've tried changing the structure of ys in several ways but nothing works.

推荐答案

您正在寻找多变量回归.AFAIK stats.linregress 没有该功能.

You're looking for multi variable regression. AFAIK stats.linregress does not have that functionality.

您可能想尝试 sklearn.linear_model.LinearRegression .检查此答案.

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