为什么这条线性回归线是直线的？

发布于 2025-01-28 00:47:47 字数 1029 浏览 2 评论 0原文

我有X和Y坐标的积分，我想通过线性回归符合一条直线，但我得到了锯齿状的线条。

我正在使用Sklearn的线性重试。

为了创建点循环的点A，将一百个随机板条的循环插入100 x 2的阵列中。我将其左侧切成XS，并将其右侧切成YS。

我希望当我打印M.Predict时，我希望有一条直线。

import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.linear_model import LinearRegression

X = []
adder = 0
for z in range(100):
    r = random.random() * 20
    r2 = random.random() * 15
    X.append([r+adder-0.4, r2+adder])
    adder += 0.6
X = np.array(X)

plt.scatter(X[:,0], X[:,1], s=10)
plt.show()

m = LinearRegression()
m.fit(X[:,0].reshape(1, -1), X[:,1].reshape(1, -1))

plt.plot(m.predict(X[:,0].reshape(1, -1))[0])

原文

I have points with x and y coordinates I want to fit a straight line to with Linear Regression but I get a jagged looking line.

I am attemting to use LinearRegression from sklearn.

To create the points run a for loop that randomly crates one hundred points into an array that is 100 x 2 in shape. I slice the left side of it for the xs and the right side of it for the ys.

I expect to have a straight line when I print m.predict.

import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.linear_model import LinearRegression

X = []
adder = 0
for z in range(100):
    r = random.random() * 20
    r2 = random.random() * 15
    X.append([r+adder-0.4, r2+adder])
    adder += 0.6
X = np.array(X)

plt.scatter(X[:,0], X[:,1], s=10)
plt.show()

m = LinearRegression()
m.fit(X[:,0].reshape(1, -1), X[:,1].reshape(1, -1))

plt.plot(m.predict(X[:,0].reshape(1, -1))[0])

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夜雨飘雪 2025-02-04 00:47:47

我对numpy不好，但是，我认为这是因为使用reshape（）函数将x [：，0]和x [：,, 1]从1d到2D，结果2D数组仅包含一个元素，而不是创建len（x [：0]）的2D数组 and code> len（x [：，1]）。并导致不希望的回归器。
我能够使用大熊猫重新创建该模型，并能够绘制所需的结果。代码如下

import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.linear_model import LinearRegression
import pandas as pd
X = []
adder = 0
for z in range(100):
    r = random.random() * 20
    r2 = random.random() * 15
    X.append([r+adder-0.4, r2+adder])
    adder += 0.6
X = np.array(X)

y_train = pd.DataFrame(X[:,1],columns=['y'])
X_train = pd.DataFrame(X[:,0],columns=['X'])

//plt.scatter(X_train, y_train, s=10)
//plt.show()

m = LinearRegression()
m.fit(X_train, y_train)
plt.scatter(X_train,y_train)
plt.plot(X_train,m.predict(X_train),color='red')

I am not good with numpy but, I think it is because the use of reshape() function to convert X[:,0] and X[:,1] from 1D to 2D, the resulting 2D array contains only one element, instead of creating a 2D array of len(X[:,0]) and len(X[:,1]) respectively. And resulting into an undesired regressor.
I am able to recreate this model using pandas and able to plot the desired result. Code as follows

import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.linear_model import LinearRegression
import pandas as pd
X = []
adder = 0
for z in range(100):
    r = random.random() * 20
    r2 = random.random() * 15
    X.append([r+adder-0.4, r2+adder])
    adder += 0.6
X = np.array(X)

y_train = pd.DataFrame(X[:,1],columns=['y'])
X_train = pd.DataFrame(X[:,0],columns=['X'])

//plt.scatter(X_train, y_train, s=10)
//plt.show()

m = LinearRegression()
m.fit(X_train, y_train)
plt.scatter(X_train,y_train)
plt.plot(X_train,m.predict(X_train),color='red')