如何定义线性回归的目标变量答案

【问题标题】：how to define target variable for linear regression如何定义线性回归的目标变量
【发布时间】：2023-04-07 07:16:01
【问题描述】：

我想对尺寸为 96x100 的数据集执行回归分析。这些列表示天数（100）的值，而自变量是时间。我如何执行线性回归，因为我的目标变量是多列。样本数据集如下：

time    day1    day2    day3    day4    day5    day6    day7    day8    day9    day10   day11   day12   day13   day14   day15   day16   day17   day18   day19   day20   day21   day22   day23   day24   day25   day26   day27   day28   day29   day30   day31   day32   day33   day34   day35   day36   day37   day38   day39   day40   day41   day42   day43   day44   day45   day46   day47   day48   day49   day50   day51   day52   day53   day54   day55   day56   day57   day58   day59   day60   day61   day62   day63   day64   day65   day66   day57   day68   day69   day70   day71   day72   day73   day74   day75   day76   day77   day78   day79   day80   day81   day82   day83   day84   day85   day86   day87   day88   day89   day90   day91   day92   day93   day94   day95   day96   day97   day98   day99   day100
500 6.07588E-10 6.13664E-10 5.89361E-10 5.95437E-10 6.31892E-10 6.37968E-10 5.83285E-10 6.01512E-10 5.83285E-10 6.1974E-10  3.03794E-09 -6.07588E-10    -2.43035E-09    1.21518E-09 2.43035E-09 6.07588E-10 6.07588E-10 -1.21518E-09    -1.21518E-09    0   3.03794E-09 1.82276E-09 -1.82276E-09    1.82276E-09 -2.43035E-09    -1.21518E-09    -1.21518E-09    -1.82276E-09    -1.21518E-09    2.43035E-09 1.82276E-09 -2.43035E-09    1.21518E-09 -6.07588E-10    -1.21518E-09    0   -1.21518E-09    1.21518E-09 -2.43035E-09    -2.43035E-09    3.03794E-09 -1.82276E-09    6.07588E-10 -1.82276E-09    3.03794E-09 -2.43035E-09    1.82276E-09 -1.82276E-09    0   0   1.82276E-09 -3.03794E-09    0   3.03794E-09 -1.21518E-09    -1.21518E-09    0   3.03794E-09 1.21518E-09 6.07588E-10 -3.03794E-09    1.21518E-09 3.03794E-09 0   6.07588E-10 -6.07588E-10    -6.07588E-10    1.82276E-09 -3.03794E-09    -1.21518E-09    1.21518E-09 1.82276E-09 1.82276E-09 2.43035E-09 3.03794E-09 1.21518E-09 1.21518E-09 -2.43035E-09    3.03794E-09 0   -1.21518E-09    -1.82276E-09    -1.82276E-09    1.82276E-09 -3.03794E-09    1.82276E-09 0   2.43035E-09 3.03794E-09 -2.43035E-09    -1.21518E-09    6.07588E-10 -1.21518E-09    6.07588E-10 3.03794E-09 0   -2.43035E-09    -1.21518E-09    -1.82276E-09    0
515 6.07588E-10 5.89361E-10 6.07588E-10 6.01512E-10 6.25816E-10 6.07588E-10 6.1974E-10  6.37968E-10 5.77209E-10 5.95437E-10 1.82276E-09 -3.03794E-09    0   2.43035E-09 1.21518E-09 -3.03794E-09    -3.03794E-09    -1.82276E-09    2.43035E-09 0   1.82276E-09 3.03794E-09 2.43035E-09 6.07588E-10 1.21518E-09 -2.43035E-09    -6.07588E-10    -1.82276E-09    -1.21518E-09    -2.43035E-09    1.82276E-09 -1.21518E-09    6.07588E-10 6.07588E-10 0   6.07588E-10 3.03794E-09 -3.03794E-09    -1.21518E-09    -1.82276E-09    0   -3.03794E-09    1.21518E-09 -2.43035E-09    -2.43035E-09    -2.43035E-09    1.82276E-09 -1.82276E-09    6.07588E-10 -3.03794E-09    -6.07588E-10    -1.21518E-09    3.03794E-09 -1.82276E-09    -6.07588E-10    -1.21518E-09    1.82276E-09 3.03794E-09 -1.21518E-09    -6.07588E-10    -1.82276E-09    -2.43035E-09    -1.21518E-09    1.82276E-09 3.03794E-09 1.21518E-09 6.07588E-10 -1.82276E-09    2.43035E-09 -3.03794E-09    0   -2.43035E-09    -1.82276E-09    -3.03794E-09    3.03794E-09 3.03794E-09 3.03794E-09 -6.07588E-10    -6.07588E-10    -6.07588E-10    -2.43035E-09    -2.43035E-09    -1.82276E-09    -3.03794E-09    -1.21518E-09    -6.07588E-10    6.07588E-10 -3.03794E-09    -1.82276E-09    6.07588E-10 2.43035E-09 1.82276E-09 1.21518E-09 0   0   1.21518E-09 3.03794E-09 2.43035E-09 6.07588E-10 3.03794E-09
530 6.07588E-10 6.01512E-10 6.1974E-10  6.13664E-10 5.95437E-10 6.31892E-10 6.01512E-10 5.77209E-10 6.13664E-10 6.25816E-10 1.82276E-09 2.43035E-09 1.82276E-09 -1.21518E-09    1.82276E-09 2.43035E-09 3.03794E-09 3.03794E-09 2.43035E-09 6.07588E-10 6.07588E-10 -6.07588E-10    2.43035E-09 0   1.82276E-09 6.07588E-10 0   3.03794E-09 -1.82276E-09    3.03794E-09 0   1.82276E-09 1.21518E-09 -2.43035E-09    -2.43035E-09    -3.03794E-09    1.21518E-09 -6.07588E-10    -1.82276E-09    2.43035E-09 3.03794E-09 -1.21518E-09    -6.07588E-10    6.07588E-10 2.43035E-09 0   -6.07588E-10    3.03794E-09 3.03794E-09 -1.82276E-09    3.03794E-09 1.82276E-09 6.07588E-10 0   -2.43035E-09    -3.03794E-09    -6.07588E-10    -2.43035E-09    -3.03794E-09    -1.21518E-09    1.82276E-09 6.07588E-10 3.03794E-09 6.07588E-10 0   3.03794E-09 2.43035E-09 0   -3.03794E-09    -3.03794E-09    1.21518E-09 -1.82276E-09    -3.03794E-09    0   -6.07588E-10    3.03794E-09 6.07588E-10 -2.43035E-09    -1.21518E-09    -2.43035E-09    -3.03794E-09    0   1.21518E-09 3.03794E-09 2.43035E-09 -1.82276E-09    -6.07588E-10    1.82276E-09 -2.43035E-09    1.21518E-09 1.21518E-09 -6.07588E-10    1.21518E-09 -3.03794E-09    -6.07588E-10    -2.43035E-09    -1.82276E-09    3.03794E-09 -2.43035E-09    3.03794E-09
545 6.07588E-10 6.25816E-10 6.07588E-10 6.13664E-10 6.07588E-10 6.01512E-10 5.95437E-10 6.07588E-10 5.95437E-10 6.01512E-10 3.03794E-09 1.21518E-09 -1.82276E-09    -3.03794E-09    3.03794E-09 1.82276E-09 1.21518E-09 6.07588E-10 6.07588E-10 -1.82276E-09    -1.21518E-09    3.03794E-09 1.82276E-09 2.43035E-09 1.21518E-09 2.43035E-09 -1.82276E-09    2.43035E-09 -3.03794E-09    1.82276E-09 -2.43035E-09    -6.07588E-10    3.03794E-09 2.43035E-09 1.21518E-09 3.03794E-09 -3.03794E-09    0   -1.82276E-09    2.43035E-09 -1.21518E-09    6.07588E-10 1.82276E-09 1.21518E-09 1.21518E-09 -6.07588E-10    -1.21518E-09    -6.07588E-10    3.03794E-09 1.21518E-09 2.43035E-09 -1.21518E-09    0   1.82276E-09 -1.82276E-09    1.21518E-09 1.21518E-09 3.03794E-09 -6.07588E-10    -1.21518E-09    6.07588E-10 -6.07588E-10    6.07588E-10 1.82276E-09 -6.07588E-10    3.03794E-09 -1.82276E-09    1.21518E-09 -6.07588E-10    1.21518E-09 1.82276E-09 -2.43035E-09    -2.43035E-09    -6.07588E-10    -6.07588E-10    6.07588E-10 6.07588E-10 3.03794E-09 -6.07588E-10    1.21518E-09 -6.07588E-10    2.43035E-09 -2.43035E-09    -2.43035E-09    -2.43035E-09    -1.82276E-09    0   -1.82276E-09    -3.03794E-09    1.21518E-09 3.03794E-09 1.21518E-09 3.03794E-09 -1.21518E-09    -3.03794E-09    -6.07588E-10    -1.21518E-09    1.21518E-09 -2.43035E-09    -6.07588E-10
600 6.07588E-10 6.13664E-10 6.07588E-10 6.01512E-10 6.13664E-10 6.01512E-10 6.1974E-10  6.1974E-10  5.77209E-10 5.89361E-10 1.21518E-09 -1.82276E-09    -2.43035E-09    1.82276E-09 3.03794E-09 -6.07588E-10    2.43035E-09 1.82276E-09 -6.07588E-10    -3.03794E-09    3.03794E-09 -3.03794E-09    -3.03794E-09    -1.21518E-09    -6.07588E-10    -1.82276E-09    1.82276E-09 3.03794E-09 -2.43035E-09    -2.43035E-09    -1.21518E-09    -3.03794E-09    -1.21518E-09    -3.03794E-09    -1.21518E-09    -3.03794E-09    -6.07588E-10    6.07588E-10 2.43035E-09 -6.07588E-10    -3.03794E-09    1.82276E-09 0   3.03794E-09 -1.21518E-09    3.03794E-09 -2.43035E-09    -1.82276E-09    -1.21518E-09    -1.82276E-09    -6.07588E-10    -1.21518E-09    1.82276E-09 0   0   -1.82276E-09    -1.21518E-09    -3.03794E-09    2.43035E-09 6.07588E-10 1.21518E-09 1.82276E-09 2.43035E-09 1.82276E-09 -1.82276E-09    1.82276E-09 0   3.03794E-09 1.82276E-09 1.82276E-09 1.82276E-09 -6.07588E-10    -1.82276E-09    -1.82276E-09    -6.07588E-10    -1.21518E-09    -1.82276E-09    3.03794E-09 -6.07588E-10    3.03794E-09 1.21518E-09 -1.82276E-09    -6.07588E-10    6.07588E-10 0   2.43035E-09 -2.43035E-09    -6.07588E-10    -3.03794E-09    2.43035E-09 -1.21518E-09    -1.82276E-09    1.21518E-09 3.03794E-09 1.82276E-09 3.03794E-09 3.03794E-09 6.07588E-10 1.21518E-09 -1.82276E-09
615 6.07588E-10 6.13664E-10 6.1974E-10  5.77209E-10 6.37968E-10 6.1974E-10  6.13664E-10 6.37968E-10 6.31892E-10 6.1974E-10  3.03794E-09 -6.07588E-10    6.07588E-10 0   1.82276E-09 -3.03794E-09    1.82276E-09 3.03794E-09 -3.03794E-09    6.07588E-10 2.43035E-09 3.03794E-09 -2.43035E-09    1.82276E-09 1.82276E-09 -1.21518E-09    -3.03794E-09    2.43035E-09 -2.43035E-09    -3.03794E-09    0   -2.43035E-09    -3.03794E-09    -6.07588E-10    -6.07588E-10    -2.43035E-09    3.03794E-09 3.03794E-09 -1.82276E-09    -1.21518E-09    6.07588E-10 -1.21518E-09    0   -1.82276E-09    0   6.07588E-10 3.03794E-09 -6.07588E-10    1.82276E-09 2.43035E-09 3.03794E-09 1.82276E-09 1.82276E-09 2.43035E-09 -3.03794E-09    -1.21518E-09    -1.21518E-09    1.82276E-09 6.07588E-10 6.07588E-10 -6.07588E-10    -3.03794E-09    1.82276E-09 3.03794E-09 -3.03794E-09    3.03794E-09 1.21518E-09 1.82276E-09 -1.82276E-09    -1.21518E-09    2.43035E-09 1.82276E-09 1.21518E-09 1.21518E-09 -1.82276E-09    1.82276E-09 1.82276E-09 3.03794E-09 -6.07588E-10    -1.82276E-09    1.21518E-09 1.21518E-09 -1.21518E-09    -2.43035E-09    -1.21518E-09    1.82276E-09 -6.07588E-10    0   2.43035E-09 -1.21518E-09    1.21518E-09 6.07588E-10 -1.21518E-09    1.82276E-09 1.82276E-09 1.21518E-09 6.07588E-10 -6.07588E-10    1.21518E-09 2.43035E-09
630 6.45127E-10 6.19322E-10 6.45127E-10 6.12871E-10 6.77384E-10 6.5803E-10  6.51578E-10 6.32225E-10 6.70932E-10 6.70932E-10 2.58051E-09 2.58051E-09 -1.29025E-09    -1.29025E-09    -3.22564E-09    1.93538E-09 3.22564E-09 -3.22564E-09    1.93538E-09 2.58051E-09 6.45127E-10 3.22564E-09 -1.29025E-09    0   3.22564E-09 6.45127E-10 -6.45127E-10    -6.45127E-10    -2.58051E-09    -1.93538E-09    -3.22564E-09    2.58051E-09 1.29025E-09 2.58051E-09 1.29025E-09 1.29025E-09 2.58051E-09 -1.29025E-09    -3.22564E-09    -3.22564E-09    1.93538E-09 -1.93538E-09    -3.22564E-09    -6.45127E-10    6.45127E-10 -1.29025E-09    -1.93538E-09    -6.45127E-10    6.45127E-10 -6.45127E-10    -3.22564E-09    1.29025E-09 2.58051E-09 -3.22564E-09    3.22564E-09 -1.93538E-09    -2.58051E-09    -2.58051E-09    -2.58051E-09    3.22564E-09 6.45127E-10 0   -3.22564E-09    3.22564E-09 -2.58051E-09    1.93538E-09 -1.93538E-09    6.45127E-10 6.45127E-10 3.22564E-09 -1.29025E-09    1.93538E-09 1.93538E-09 2.58051E-09 -3.22564E-09    0   -3.22564E-09    -2.58051E-09    1.93538E-09 1.93538E-09 -2.58051E-09    1.93538E-09 1.29025E-09 3.22564E-09 1.93538E-09 -1.93538E-09    3.22564E-09 -3.22564E-09    1.29025E-09 -1.29025E-09    3.22564E-09 -1.93538E-09    -3.22564E-09    -6.45127E-10    3.22564E-09 -3.22564E-09    -1.29025E-09    -2.58051E-09    -3.22564E-09    6.45127E-10

plt.scatter(reg.predict(X), reg.predict(X) - y, 
            color = "green", s = 10, label = 'Train data')

【问题讨论】：

您试图从这些数据中预测什么？
@G.Anderson the independent variable is the time
我正在尝试预测水流是否正常。 day1 到 day100 列中的值对应于第 1 天凌晨 5:00 的水流量值，依此类推。为简单起见，我将时间编码为 500,515。
@QuangHoang "值被预测的变量称为因变量，已知值用于预测的变量称为自变量。" （来自 this site ）所以你有数据切换
@G.Anderson ha，你是对的。我在想什么:-)。

标签： python pandas machine-learning scikit-learn linear-regression

【解决方案1】：

您可以将时间作为因变量（标签）和不同日期的值，即模型的特征（自变量）。您可以很容易地使用 sklearn 进行多级回归：

from sklearn.linear_model import LinearRegression
X=np.array(df.drop("time",1))
y=np.array(df["time"])
clf=LinearRegression()
clf.fit(X,y)

【讨论】：

这行代码X=np.array(df.drop("time",1))做了什么？