多项式回归排序 x

Question

在对我的数据运行 k-means 并将我的观察结果划分为不同的集群之后，我一直在尝试绘制我的多项式回归。然而，它非常混乱，而不是我期望多项式线的样子。我知道排序 x 可能会帮助我解决这个问题。但是我如何对 x 轴进行排序？我一直在寻找答案，但没有找到任何可以帮助我的东西。我是 python 的新手，几周来我一直在努力寻找一个好的情节，这让我发疯了。

情节：

代码：

import numpy as np
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt

df = pd.read_csv('D:\Mall_Customers.csv', usecols = ['Age', 'Annual Income (k$)','Spending Score (1-100)'])


x = StandardScaler().fit_transform(df)


kmeans = KMeans(n_clusters=5, max_iter=100, random_state=0)
y_kmeans= kmeans.fit_predict(x)

mydict = {i: np.where(kmeans.labels_ == i)[0] for i in range(kmeans.n_clusters)}

dictlist = []
for key, value in mydict.items():
    temp = [key,value]
    dictlist.append(temp)

df0 = df[df.index.isin(mydict[2].tolist())]
X = df0[['Age', 'Annual Income (k$)',]]
Y = df0['Spending Score (1-100)']



from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state=0)

poly_reg = PolynomialFeatures()
X_polynom = poly_reg.fit_transform(x_train)

PolyReg = LinearRegression()
PolyReg.fit(X_polynom, y_train)


plt.scatter(x_train.iloc[:, 1], y_train, color='red')

plt.plot(x_train, PolyReg.predict(poly_reg.fit_transform(x_train)),   color='blue')
plt.xlabel('Age. Annual income')
plt.ylabel('Spending Score')
plt.show()

Answer 1

我不知道为什么您会认为多项式回归对这些数据进行了任何业务，但是如果您想绘制其结果，则根据一组漂亮、均匀分布的点而不是 @ 进行预测会更有意义987654321@。试试这个而不是绘制蓝线的线：

x_reg_line = np.arange(40, 80)[:, None]
y_reg_line = PolyReg.predict(x_reg_line) # Don't fit again here! You have already fit your model earlier!
plt.plot(x_reg_line, y_reg_line, color="blue")