Plotting Kernel Ridge Regression results in weird lines-CodePudding

I'm trying to run kernel Ridge regression on a simple artificial dataset. When I run the code, I get two plots. The first is for Linear Regression fit, which looks normal. however, the kernel one is very erratic. Is this expected behavior, or am I not calling the functions properly?

The first plt.show():

The second plt.show():

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


w = 5
x = np.random.randn(10, 1)
x_to_draw_line = np.random.randn(1000, 1)
y = w * x

lr = LinearRegression()
lr.fit(x, y)
lr_preds = lr.predict(x_to_draw_line)
plt.figure()
plt.plot(x_to_draw_line, lr_preds, color="C1")
plt.scatter(x, y, color="C0")
plt.show()


krr = KernelRidge(kernel="polynomial")
krr.fit(x, y)
krr_preds = krr.predict(x_to_draw_line)
plt.figure()
plt.plot(x_to_draw_line, krr_preds, color="C1")
plt.scatter(x, y, color="C0")
plt.show()

CodePudding user response：

The line plot appears jumbled because matplotlib draws a connecting line between each pair of points in the order they appear in the input array.

The solution is to sort the array of randomly generated x-values for which to generate and draw predictions:

x_to_draw_line = np.random.randn(1000, 1).sort()